{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# python期末项目--如何选择你未来的城市"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**项目介绍** \n",
    "- 项目人：罗佳莼\n",
    "- 时间：2020年7月\n",
    "- 数据源：来源于[国家数据库](http://data.stats.gov.cn/)\n",
    "- 目标：帮助即将毕业的大学生选择合适的工作/居住城市"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 导入模块"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 有关pandas的学习可以[参考文档](https://www.pypandas.cn/)\n",
    "- 关于地图绘制有帮助的文档：[pycharts官方文档](http://pyecharts.org/)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyecharts.charts import Geo\n",
    "from pyecharts.faker import Faker\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Geo\n",
    "from pyecharts.globals import ChartType, SymbolType"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 定义css"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 更改jupyter notebook的样式可以参考大佬的文章[Anaconda jupyter notebook 简单更改前端样式](https://blog.csdn.net/And_ZJ/article/details/83591730)，感谢经验分享！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "/* 本电子讲义使用之CSS */\n",
       "div.code_cell {\n",
       "    background-color: #fceae6\n",
       "}\n",
       "div.cell.selected {\n",
       "    background-color: #e6eefe;\n",
       "    font-size: 2rem;\n",
       "    line-height: 2.4rem;\n",
       "}\n",
       "div.cell.selected .rendered_html table {\n",
       "    font-size: 2rem !important;\n",
       "    line-height: 2.4rem !important;\n",
       "}\n",
       ".rendered_html pre code {\n",
       "    background-color: #C4E4ff;   \n",
       "    padding: 2px 25px;\n",
       "}\n",
       ".rendered_html pre {\n",
       "    background-color: #99c9ff;\n",
       "}\n",
       "div.code_cell .CodeMirror {\n",
       "    font-size: 2rem !important;\n",
       "    line-height: 2.4rem !important;\n",
       "}\n",
       ".rendered_html img, .rendered_html svg {\n",
       "    max-width: 60%;\n",
       "    height: auto;\n",
       "    float: right;\n",
       "}\n",
       "\n",
       ".rendered_html img[src*=\"#full\"], .rendered_html svg[src*=\"#full\"] {\n",
       "    max-width: 95%;\n",
       "    height: auto;\n",
       "}\n",
       "\n",
       ".rendered_html img[src*=\"#thumbnail\"], .rendered_html svg[src*=\"#thumbnail\"] {\n",
       "    max-width: 15%;\n",
       "    height: auto;\n",
       "}\n",
       "\n",
       "/* Gradient transparent - color - transparent */\n",
       "hr {\n",
       "    border: 0;\n",
       "    border-bottom: 1px dashed #ccc;\n",
       "}\n",
       ".emoticon{\n",
       "    font-size: 5rem;\n",
       "    line-height: 4.4rem;\n",
       "    text-align: center;\n",
       "    vertical-align: middle;\n",
       "}\n",
       ".bg-split_apply_comine {\n",
       "    width: 500px;     \n",
       "    height: 300px;\n",
       "    background: url('02_split-apply-comine_500x300.png') -10px -10px;\n",
       "    float: right;\n",
       "}\n",
       ".bg-comine {\n",
       "    width: 175px;\n",
       "    height: 150px;\n",
       "    background: url('02_split-apply-comine_500x300.png') -280px -80px;\n",
       "    float: right;\n",
       "}\n",
       ".bg-apply {\n",
       "    width: 155px;\n",
       "    height: 225px;\n",
       "    background: url('02_split-apply-comine_500x300.png') -160px -30px;\n",
       "    float: right;\n",
       "}\n",
       ".bg-split {\n",
       "    width: 205px;\n",
       "    height: 225px;\n",
       "    background: url('02_split-apply-comine_500x300.png') -10px -30px;\n",
       "    float: right;\n",
       "}\n",
       ".break {\n",
       "                   page-break-after: right; \n",
       "                   width:700px;\n",
       "                   clear:both;\n",
       "}\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<style>\n",
    "/* 本电子讲义使用之CSS */\n",
    "div.code_cell {\n",
    "    background-color: #fceae6\n",
    "}\n",
    "div.cell.selected {\n",
    "    background-color: #e6eefe;\n",
    "    font-size: 2rem;\n",
    "    line-height: 2.4rem;\n",
    "}\n",
    "div.cell.selected .rendered_html table {\n",
    "    font-size: 2rem !important;\n",
    "    line-height: 2.4rem !important;\n",
    "}\n",
    ".rendered_html pre code {\n",
    "    background-color: #C4E4ff;   \n",
    "    padding: 2px 25px;\n",
    "}\n",
    ".rendered_html pre {\n",
    "    background-color: #99c9ff;\n",
    "}\n",
    "div.code_cell .CodeMirror {\n",
    "    font-size: 2rem !important;\n",
    "    line-height: 2.4rem !important;\n",
    "}\n",
    ".rendered_html img, .rendered_html svg {\n",
    "    max-width: 60%;\n",
    "    height: auto;\n",
    "    float: right;\n",
    "}\n",
    "\n",
    ".rendered_html img[src*=\"#full\"], .rendered_html svg[src*=\"#full\"] {\n",
    "    max-width: 95%;\n",
    "    height: auto;\n",
    "}\n",
    "\n",
    ".rendered_html img[src*=\"#thumbnail\"], .rendered_html svg[src*=\"#thumbnail\"] {\n",
    "    max-width: 15%;\n",
    "    height: auto;\n",
    "}\n",
    "\n",
    "/* Gradient transparent - color - transparent */\n",
    "hr {\n",
    "    border: 0;\n",
    "    border-bottom: 1px dashed #ccc;\n",
    "}\n",
    ".emoticon{\n",
    "    font-size: 5rem;\n",
    "    line-height: 4.4rem;\n",
    "    text-align: center;\n",
    "    vertical-align: middle;\n",
    "}\n",
    ".bg-split_apply_comine {\n",
    "    width: 500px;     \n",
    "    height: 300px;\n",
    "    background: url('02_split-apply-comine_500x300.png') -10px -10px;\n",
    "    float: right;\n",
    "}\n",
    ".bg-comine {\n",
    "    width: 175px;\n",
    "    height: 150px;\n",
    "    background: url('02_split-apply-comine_500x300.png') -280px -80px;\n",
    "    float: right;\n",
    "}\n",
    ".bg-apply {\n",
    "    width: 155px;\n",
    "    height: 225px;\n",
    "    background: url('02_split-apply-comine_500x300.png') -160px -30px;\n",
    "    float: right;\n",
    "}\n",
    ".bg-split {\n",
    "    width: 205px;\n",
    "    height: 225px;\n",
    "    background: url('02_split-apply-comine_500x300.png') -10px -30px;\n",
    "    float: right;\n",
    "}\n",
    ".break {\n",
    "                   page-break-after: right; \n",
    "                   width:700px;\n",
    "                   clear:both;\n",
    "}\n",
    "</style>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据读取"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 有关pandas的数据读取的学习[Python数据分析库pandas--pandas数据读写](https://www.cnblogs.com/dan-baishucaizi/p/9398801.html)，感谢分享！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>地区</th>\n",
       "      <th>年份</th>\n",
       "      <th>农村居民消费水平(元)</th>\n",
       "      <th>城镇居民消费水平(元)</th>\n",
       "      <th>地区生产总值(亿元)</th>\n",
       "      <th>年末常住人口(万人)</th>\n",
       "      <th>城镇人口(万人)</th>\n",
       "      <th>乡村人口(万人)</th>\n",
       "      <th>人口出生率(‰)</th>\n",
       "      <th>人口死亡率(‰)</th>\n",
       "      <th>...</th>\n",
       "      <th>城镇登记失业人数(万人)</th>\n",
       "      <th>供水综合生产能力(万立方米/日)</th>\n",
       "      <th>人工煤气生产能力(万立方米/日)</th>\n",
       "      <th>建成区绿化覆盖率(%)</th>\n",
       "      <th>旱灾受灾面积(千公顷 )</th>\n",
       "      <th>洪涝、山体滑坡、泥石流和台风受灾面积(千公顷 )</th>\n",
       "      <th>地震灾害次数(次)</th>\n",
       "      <th>结婚登记(万对 )</th>\n",
       "      <th>离婚登记(万对)</th>\n",
       "      <th>粗离婚率(‰)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>北京市</td>\n",
       "      <td>2017</td>\n",
       "      <td>26132</td>\n",
       "      <td>57100</td>\n",
       "      <td>28014.94</td>\n",
       "      <td>2171</td>\n",
       "      <td>1878</td>\n",
       "      <td>293</td>\n",
       "      <td>9.06</td>\n",
       "      <td>5.30</td>\n",
       "      <td>...</td>\n",
       "      <td>8.10</td>\n",
       "      <td>2178.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.15</td>\n",
       "      <td>8.06</td>\n",
       "      <td>3.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>北京市</td>\n",
       "      <td>2016</td>\n",
       "      <td>24285</td>\n",
       "      <td>52721</td>\n",
       "      <td>25669.13</td>\n",
       "      <td>2173</td>\n",
       "      <td>1880</td>\n",
       "      <td>293</td>\n",
       "      <td>9.32</td>\n",
       "      <td>5.20</td>\n",
       "      <td>...</td>\n",
       "      <td>7.99</td>\n",
       "      <td>2452.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.62</td>\n",
       "      <td>10.58</td>\n",
       "      <td>4.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>北京市</td>\n",
       "      <td>2015</td>\n",
       "      <td>22315</td>\n",
       "      <td>41846</td>\n",
       "      <td>23014.59</td>\n",
       "      <td>2171</td>\n",
       "      <td>1877</td>\n",
       "      <td>293</td>\n",
       "      <td>7.96</td>\n",
       "      <td>4.95</td>\n",
       "      <td>...</td>\n",
       "      <td>7.85</td>\n",
       "      <td>2496.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.60</td>\n",
       "      <td>8.22</td>\n",
       "      <td>3.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>北京市</td>\n",
       "      <td>2014</td>\n",
       "      <td>20506</td>\n",
       "      <td>38515</td>\n",
       "      <td>21330.83</td>\n",
       "      <td>2152</td>\n",
       "      <td>1858</td>\n",
       "      <td>294</td>\n",
       "      <td>9.75</td>\n",
       "      <td>4.92</td>\n",
       "      <td>...</td>\n",
       "      <td>7.43</td>\n",
       "      <td>2439.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.1</td>\n",
       "      <td>26.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.00</td>\n",
       "      <td>6.56</td>\n",
       "      <td>3.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>北京市</td>\n",
       "      <td>2013</td>\n",
       "      <td>17663</td>\n",
       "      <td>35836</td>\n",
       "      <td>19800.81</td>\n",
       "      <td>2115</td>\n",
       "      <td>1825</td>\n",
       "      <td>290</td>\n",
       "      <td>8.93</td>\n",
       "      <td>4.52</td>\n",
       "      <td>...</td>\n",
       "      <td>7.53</td>\n",
       "      <td>2554.53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.37</td>\n",
       "      <td>6.46</td>\n",
       "      <td>3.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    地区    年份  农村居民消费水平(元)  城镇居民消费水平(元)  地区生产总值(亿元)  年末常住人口(万人)  城镇人口(万人)  \\\n",
       "0  北京市  2017        26132        57100    28014.94        2171      1878   \n",
       "1  北京市  2016        24285        52721    25669.13        2173      1880   \n",
       "2  北京市  2015        22315        41846    23014.59        2171      1877   \n",
       "3  北京市  2014        20506        38515    21330.83        2152      1858   \n",
       "4  北京市  2013        17663        35836    19800.81        2115      1825   \n",
       "\n",
       "   乡村人口(万人)  人口出生率(‰)  人口死亡率(‰)  ...  城镇登记失业人数(万人)  供水综合生产能力(万立方米/日)  \\\n",
       "0       293      9.06      5.30  ...          8.10           2178.74   \n",
       "1       293      9.32      5.20  ...          7.99           2452.45   \n",
       "2       293      7.96      4.95  ...          7.85           2496.71   \n",
       "3       294      9.75      4.92  ...          7.43           2439.77   \n",
       "4       290      8.93      4.52  ...          7.53           2554.53   \n",
       "\n",
       "   人工煤气生产能力(万立方米/日)  建成区绿化覆盖率(%)  旱灾受灾面积(千公顷 )  洪涝、山体滑坡、泥石流和台风受灾面积(千公顷 )  \\\n",
       "0               NaN         48.4           NaN                       NaN   \n",
       "1               NaN         48.4           NaN                      16.1   \n",
       "2               NaN         48.4           0.2                       0.7   \n",
       "3               NaN         49.1          26.1                       NaN   \n",
       "4               NaN         47.1           NaN                       9.8   \n",
       "\n",
       "   地震灾害次数(次)  结婚登记(万对 )  离婚登记(万对)  粗离婚率(‰)  \n",
       "0        NaN      15.15      8.06     3.71  \n",
       "1        NaN      16.62     10.58     4.89  \n",
       "2        NaN      16.60      8.22     3.79  \n",
       "3        NaN      17.00      6.56     3.08  \n",
       "4        NaN      16.37      6.46     3.06  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(\"省份选择.xlsx\", encoding=\"utf8\", sep=\"\\t\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据整理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分进合击\n",
    "- 分析比较各年份地区的地区生产总值\n",
    "- 可供参考的agg的使用方法[python3 关于agg函数的用法](https://blog.csdn.net/qq_24753293/article/details/80323487)\n",
    "- 可供参考的groupby函数使用方法[python中groupby函数详解](https://www.cnblogs.com/Yanjy-OnlyOne/p/11217802.html)\n",
    "- 可供参考的pivot的使用[python pandas库——pivot使用心得](https://blog.csdn.net/qq_29118049/article/details/78804768)，感谢分享！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(31, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>年份</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>地区</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>上海市</th>\n",
       "      <td>21818.15</td>\n",
       "      <td>23567.70</td>\n",
       "      <td>25123.45</td>\n",
       "      <td>28178.65</td>\n",
       "      <td>30632.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云南省</th>\n",
       "      <td>11832.31</td>\n",
       "      <td>12814.59</td>\n",
       "      <td>13619.17</td>\n",
       "      <td>14788.42</td>\n",
       "      <td>16376.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>内蒙古自治区</th>\n",
       "      <td>16916.50</td>\n",
       "      <td>17770.19</td>\n",
       "      <td>17831.51</td>\n",
       "      <td>18128.10</td>\n",
       "      <td>16096.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京市</th>\n",
       "      <td>19800.81</td>\n",
       "      <td>21330.83</td>\n",
       "      <td>23014.59</td>\n",
       "      <td>25669.13</td>\n",
       "      <td>28014.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>吉林省</th>\n",
       "      <td>13046.40</td>\n",
       "      <td>13803.14</td>\n",
       "      <td>14063.13</td>\n",
       "      <td>14776.80</td>\n",
       "      <td>14944.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>四川省</th>\n",
       "      <td>26392.07</td>\n",
       "      <td>28536.66</td>\n",
       "      <td>30053.10</td>\n",
       "      <td>32934.54</td>\n",
       "      <td>36980.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>天津市</th>\n",
       "      <td>14442.01</td>\n",
       "      <td>15726.93</td>\n",
       "      <td>16538.19</td>\n",
       "      <td>17885.39</td>\n",
       "      <td>18549.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>宁夏回族自治区</th>\n",
       "      <td>2577.57</td>\n",
       "      <td>2752.10</td>\n",
       "      <td>2911.77</td>\n",
       "      <td>3168.59</td>\n",
       "      <td>3443.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>安徽省</th>\n",
       "      <td>19229.34</td>\n",
       "      <td>20848.75</td>\n",
       "      <td>22005.63</td>\n",
       "      <td>24407.62</td>\n",
       "      <td>27018.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>山东省</th>\n",
       "      <td>55230.32</td>\n",
       "      <td>59426.59</td>\n",
       "      <td>63002.33</td>\n",
       "      <td>68024.49</td>\n",
       "      <td>72634.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>山西省</th>\n",
       "      <td>12665.25</td>\n",
       "      <td>12761.49</td>\n",
       "      <td>12766.49</td>\n",
       "      <td>13050.41</td>\n",
       "      <td>15528.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广东省</th>\n",
       "      <td>62474.79</td>\n",
       "      <td>67809.85</td>\n",
       "      <td>72812.55</td>\n",
       "      <td>80854.91</td>\n",
       "      <td>89705.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广西壮族自治区</th>\n",
       "      <td>14449.90</td>\n",
       "      <td>15672.89</td>\n",
       "      <td>16803.12</td>\n",
       "      <td>18317.64</td>\n",
       "      <td>18523.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新疆维吾尔自治区</th>\n",
       "      <td>8443.84</td>\n",
       "      <td>9273.46</td>\n",
       "      <td>9324.80</td>\n",
       "      <td>9649.70</td>\n",
       "      <td>10881.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>江苏省</th>\n",
       "      <td>59753.37</td>\n",
       "      <td>65088.32</td>\n",
       "      <td>70116.38</td>\n",
       "      <td>77388.28</td>\n",
       "      <td>85869.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>江西省</th>\n",
       "      <td>14410.19</td>\n",
       "      <td>15714.63</td>\n",
       "      <td>16723.78</td>\n",
       "      <td>18499.00</td>\n",
       "      <td>20006.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>河北省</th>\n",
       "      <td>28442.95</td>\n",
       "      <td>29421.15</td>\n",
       "      <td>29806.11</td>\n",
       "      <td>32070.45</td>\n",
       "      <td>34016.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>河南省</th>\n",
       "      <td>32191.30</td>\n",
       "      <td>34938.24</td>\n",
       "      <td>37002.16</td>\n",
       "      <td>40471.79</td>\n",
       "      <td>44552.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>浙江省</th>\n",
       "      <td>37756.59</td>\n",
       "      <td>40173.03</td>\n",
       "      <td>42886.49</td>\n",
       "      <td>47251.36</td>\n",
       "      <td>51768.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>海南省</th>\n",
       "      <td>3177.56</td>\n",
       "      <td>3500.72</td>\n",
       "      <td>3702.76</td>\n",
       "      <td>4053.20</td>\n",
       "      <td>4462.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>湖北省</th>\n",
       "      <td>24791.83</td>\n",
       "      <td>27379.22</td>\n",
       "      <td>29550.19</td>\n",
       "      <td>32665.38</td>\n",
       "      <td>35478.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>湖南省</th>\n",
       "      <td>24621.67</td>\n",
       "      <td>27037.32</td>\n",
       "      <td>28902.21</td>\n",
       "      <td>31551.37</td>\n",
       "      <td>33902.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>甘肃省</th>\n",
       "      <td>6330.69</td>\n",
       "      <td>6836.82</td>\n",
       "      <td>6790.32</td>\n",
       "      <td>7200.37</td>\n",
       "      <td>7459.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>福建省</th>\n",
       "      <td>21868.49</td>\n",
       "      <td>24055.76</td>\n",
       "      <td>25979.82</td>\n",
       "      <td>28810.58</td>\n",
       "      <td>32182.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西藏自治区</th>\n",
       "      <td>815.67</td>\n",
       "      <td>920.83</td>\n",
       "      <td>1026.39</td>\n",
       "      <td>1151.41</td>\n",
       "      <td>1310.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贵州省</th>\n",
       "      <td>8086.86</td>\n",
       "      <td>9266.39</td>\n",
       "      <td>10502.56</td>\n",
       "      <td>11776.73</td>\n",
       "      <td>13540.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>辽宁省</th>\n",
       "      <td>27213.22</td>\n",
       "      <td>28626.58</td>\n",
       "      <td>28669.02</td>\n",
       "      <td>22246.90</td>\n",
       "      <td>23409.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>重庆市</th>\n",
       "      <td>12783.26</td>\n",
       "      <td>14262.60</td>\n",
       "      <td>15717.27</td>\n",
       "      <td>17740.59</td>\n",
       "      <td>19424.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>陕西省</th>\n",
       "      <td>16205.45</td>\n",
       "      <td>17689.94</td>\n",
       "      <td>18021.86</td>\n",
       "      <td>19399.59</td>\n",
       "      <td>21898.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>青海省</th>\n",
       "      <td>2122.06</td>\n",
       "      <td>2303.32</td>\n",
       "      <td>2417.05</td>\n",
       "      <td>2572.49</td>\n",
       "      <td>2624.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>黑龙江省</th>\n",
       "      <td>14454.91</td>\n",
       "      <td>15039.38</td>\n",
       "      <td>15083.67</td>\n",
       "      <td>15386.09</td>\n",
       "      <td>15902.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "年份            2013      2014      2015      2016      2017\n",
       "地区                                                        \n",
       "上海市       21818.15  23567.70  25123.45  28178.65  30632.99\n",
       "云南省       11832.31  12814.59  13619.17  14788.42  16376.34\n",
       "内蒙古自治区    16916.50  17770.19  17831.51  18128.10  16096.21\n",
       "北京市       19800.81  21330.83  23014.59  25669.13  28014.94\n",
       "吉林省       13046.40  13803.14  14063.13  14776.80  14944.53\n",
       "四川省       26392.07  28536.66  30053.10  32934.54  36980.22\n",
       "天津市       14442.01  15726.93  16538.19  17885.39  18549.19\n",
       "宁夏回族自治区    2577.57   2752.10   2911.77   3168.59   3443.56\n",
       "安徽省       19229.34  20848.75  22005.63  24407.62  27018.00\n",
       "山东省       55230.32  59426.59  63002.33  68024.49  72634.15\n",
       "山西省       12665.25  12761.49  12766.49  13050.41  15528.42\n",
       "广东省       62474.79  67809.85  72812.55  80854.91  89705.23\n",
       "广西壮族自治区   14449.90  15672.89  16803.12  18317.64  18523.26\n",
       "新疆维吾尔自治区   8443.84   9273.46   9324.80   9649.70  10881.96\n",
       "江苏省       59753.37  65088.32  70116.38  77388.28  85869.76\n",
       "江西省       14410.19  15714.63  16723.78  18499.00  20006.31\n",
       "河北省       28442.95  29421.15  29806.11  32070.45  34016.32\n",
       "河南省       32191.30  34938.24  37002.16  40471.79  44552.83\n",
       "浙江省       37756.59  40173.03  42886.49  47251.36  51768.26\n",
       "海南省        3177.56   3500.72   3702.76   4053.20   4462.54\n",
       "湖北省       24791.83  27379.22  29550.19  32665.38  35478.09\n",
       "湖南省       24621.67  27037.32  28902.21  31551.37  33902.96\n",
       "甘肃省        6330.69   6836.82   6790.32   7200.37   7459.90\n",
       "福建省       21868.49  24055.76  25979.82  28810.58  32182.09\n",
       "西藏自治区       815.67    920.83   1026.39   1151.41   1310.92\n",
       "贵州省        8086.86   9266.39  10502.56  11776.73  13540.83\n",
       "辽宁省       27213.22  28626.58  28669.02  22246.90  23409.24\n",
       "重庆市       12783.26  14262.60  15717.27  17740.59  19424.73\n",
       "陕西省       16205.45  17689.94  18021.86  19399.59  21898.81\n",
       "青海省        2122.06   2303.32   2417.05   2572.49   2624.83\n",
       "黑龙江省      14454.91  15039.38  15083.67  15386.09  15902.68"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A-1 採用进阶分进合击分析策略 groupby ... agg  ... pivot\n",
    "分进合击进阶pv = df.groupby(by= [\"地区\",\"年份\"]) \\\n",
    "                  .agg({\"地区生产总值(亿元)\":\"sum\"}) \\\n",
    "                  .reset_index() \\\n",
    "                  .set_index([\"地区\"]) \\\n",
    "                  .pivot(columns=\"年份\", values=\"地区生产总值(亿元)\")\n",
    "print (分进合击进阶pv.shape)\n",
    "分进合击进阶pv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分进合击pivot_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 有关数据透视表的讲解介绍：[一文看懂pandas的透视表pivot_table](https://www.cnblogs.com/Yanjy-OnlyOne/p/11195621.html)，感谢经验分享！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(31, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>年份</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>地区</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>上海市</th>\n",
       "      <td>21818.15</td>\n",
       "      <td>23567.70</td>\n",
       "      <td>25123.45</td>\n",
       "      <td>28178.65</td>\n",
       "      <td>30632.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云南省</th>\n",
       "      <td>11832.31</td>\n",
       "      <td>12814.59</td>\n",
       "      <td>13619.17</td>\n",
       "      <td>14788.42</td>\n",
       "      <td>16376.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>内蒙古自治区</th>\n",
       "      <td>16916.50</td>\n",
       "      <td>17770.19</td>\n",
       "      <td>17831.51</td>\n",
       "      <td>18128.10</td>\n",
       "      <td>16096.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京市</th>\n",
       "      <td>19800.81</td>\n",
       "      <td>21330.83</td>\n",
       "      <td>23014.59</td>\n",
       "      <td>25669.13</td>\n",
       "      <td>28014.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>吉林省</th>\n",
       "      <td>13046.40</td>\n",
       "      <td>13803.14</td>\n",
       "      <td>14063.13</td>\n",
       "      <td>14776.80</td>\n",
       "      <td>14944.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>四川省</th>\n",
       "      <td>26392.07</td>\n",
       "      <td>28536.66</td>\n",
       "      <td>30053.10</td>\n",
       "      <td>32934.54</td>\n",
       "      <td>36980.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>天津市</th>\n",
       "      <td>14442.01</td>\n",
       "      <td>15726.93</td>\n",
       "      <td>16538.19</td>\n",
       "      <td>17885.39</td>\n",
       "      <td>18549.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>宁夏回族自治区</th>\n",
       "      <td>2577.57</td>\n",
       "      <td>2752.10</td>\n",
       "      <td>2911.77</td>\n",
       "      <td>3168.59</td>\n",
       "      <td>3443.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>安徽省</th>\n",
       "      <td>19229.34</td>\n",
       "      <td>20848.75</td>\n",
       "      <td>22005.63</td>\n",
       "      <td>24407.62</td>\n",
       "      <td>27018.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>山东省</th>\n",
       "      <td>55230.32</td>\n",
       "      <td>59426.59</td>\n",
       "      <td>63002.33</td>\n",
       "      <td>68024.49</td>\n",
       "      <td>72634.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>山西省</th>\n",
       "      <td>12665.25</td>\n",
       "      <td>12761.49</td>\n",
       "      <td>12766.49</td>\n",
       "      <td>13050.41</td>\n",
       "      <td>15528.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广东省</th>\n",
       "      <td>62474.79</td>\n",
       "      <td>67809.85</td>\n",
       "      <td>72812.55</td>\n",
       "      <td>80854.91</td>\n",
       "      <td>89705.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广西壮族自治区</th>\n",
       "      <td>14449.90</td>\n",
       "      <td>15672.89</td>\n",
       "      <td>16803.12</td>\n",
       "      <td>18317.64</td>\n",
       "      <td>18523.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新疆维吾尔自治区</th>\n",
       "      <td>8443.84</td>\n",
       "      <td>9273.46</td>\n",
       "      <td>9324.80</td>\n",
       "      <td>9649.70</td>\n",
       "      <td>10881.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>江苏省</th>\n",
       "      <td>59753.37</td>\n",
       "      <td>65088.32</td>\n",
       "      <td>70116.38</td>\n",
       "      <td>77388.28</td>\n",
       "      <td>85869.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>江西省</th>\n",
       "      <td>14410.19</td>\n",
       "      <td>15714.63</td>\n",
       "      <td>16723.78</td>\n",
       "      <td>18499.00</td>\n",
       "      <td>20006.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>河北省</th>\n",
       "      <td>28442.95</td>\n",
       "      <td>29421.15</td>\n",
       "      <td>29806.11</td>\n",
       "      <td>32070.45</td>\n",
       "      <td>34016.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>河南省</th>\n",
       "      <td>32191.30</td>\n",
       "      <td>34938.24</td>\n",
       "      <td>37002.16</td>\n",
       "      <td>40471.79</td>\n",
       "      <td>44552.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>浙江省</th>\n",
       "      <td>37756.59</td>\n",
       "      <td>40173.03</td>\n",
       "      <td>42886.49</td>\n",
       "      <td>47251.36</td>\n",
       "      <td>51768.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>海南省</th>\n",
       "      <td>3177.56</td>\n",
       "      <td>3500.72</td>\n",
       "      <td>3702.76</td>\n",
       "      <td>4053.20</td>\n",
       "      <td>4462.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>湖北省</th>\n",
       "      <td>24791.83</td>\n",
       "      <td>27379.22</td>\n",
       "      <td>29550.19</td>\n",
       "      <td>32665.38</td>\n",
       "      <td>35478.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>湖南省</th>\n",
       "      <td>24621.67</td>\n",
       "      <td>27037.32</td>\n",
       "      <td>28902.21</td>\n",
       "      <td>31551.37</td>\n",
       "      <td>33902.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>甘肃省</th>\n",
       "      <td>6330.69</td>\n",
       "      <td>6836.82</td>\n",
       "      <td>6790.32</td>\n",
       "      <td>7200.37</td>\n",
       "      <td>7459.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>福建省</th>\n",
       "      <td>21868.49</td>\n",
       "      <td>24055.76</td>\n",
       "      <td>25979.82</td>\n",
       "      <td>28810.58</td>\n",
       "      <td>32182.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西藏自治区</th>\n",
       "      <td>815.67</td>\n",
       "      <td>920.83</td>\n",
       "      <td>1026.39</td>\n",
       "      <td>1151.41</td>\n",
       "      <td>1310.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贵州省</th>\n",
       "      <td>8086.86</td>\n",
       "      <td>9266.39</td>\n",
       "      <td>10502.56</td>\n",
       "      <td>11776.73</td>\n",
       "      <td>13540.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>辽宁省</th>\n",
       "      <td>27213.22</td>\n",
       "      <td>28626.58</td>\n",
       "      <td>28669.02</td>\n",
       "      <td>22246.90</td>\n",
       "      <td>23409.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>重庆市</th>\n",
       "      <td>12783.26</td>\n",
       "      <td>14262.60</td>\n",
       "      <td>15717.27</td>\n",
       "      <td>17740.59</td>\n",
       "      <td>19424.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>陕西省</th>\n",
       "      <td>16205.45</td>\n",
       "      <td>17689.94</td>\n",
       "      <td>18021.86</td>\n",
       "      <td>19399.59</td>\n",
       "      <td>21898.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>青海省</th>\n",
       "      <td>2122.06</td>\n",
       "      <td>2303.32</td>\n",
       "      <td>2417.05</td>\n",
       "      <td>2572.49</td>\n",
       "      <td>2624.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>黑龙江省</th>\n",
       "      <td>14454.91</td>\n",
       "      <td>15039.38</td>\n",
       "      <td>15083.67</td>\n",
       "      <td>15386.09</td>\n",
       "      <td>15902.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "年份            2013      2014      2015      2016      2017\n",
       "地区                                                        \n",
       "上海市       21818.15  23567.70  25123.45  28178.65  30632.99\n",
       "云南省       11832.31  12814.59  13619.17  14788.42  16376.34\n",
       "内蒙古自治区    16916.50  17770.19  17831.51  18128.10  16096.21\n",
       "北京市       19800.81  21330.83  23014.59  25669.13  28014.94\n",
       "吉林省       13046.40  13803.14  14063.13  14776.80  14944.53\n",
       "四川省       26392.07  28536.66  30053.10  32934.54  36980.22\n",
       "天津市       14442.01  15726.93  16538.19  17885.39  18549.19\n",
       "宁夏回族自治区    2577.57   2752.10   2911.77   3168.59   3443.56\n",
       "安徽省       19229.34  20848.75  22005.63  24407.62  27018.00\n",
       "山东省       55230.32  59426.59  63002.33  68024.49  72634.15\n",
       "山西省       12665.25  12761.49  12766.49  13050.41  15528.42\n",
       "广东省       62474.79  67809.85  72812.55  80854.91  89705.23\n",
       "广西壮族自治区   14449.90  15672.89  16803.12  18317.64  18523.26\n",
       "新疆维吾尔自治区   8443.84   9273.46   9324.80   9649.70  10881.96\n",
       "江苏省       59753.37  65088.32  70116.38  77388.28  85869.76\n",
       "江西省       14410.19  15714.63  16723.78  18499.00  20006.31\n",
       "河北省       28442.95  29421.15  29806.11  32070.45  34016.32\n",
       "河南省       32191.30  34938.24  37002.16  40471.79  44552.83\n",
       "浙江省       37756.59  40173.03  42886.49  47251.36  51768.26\n",
       "海南省        3177.56   3500.72   3702.76   4053.20   4462.54\n",
       "湖北省       24791.83  27379.22  29550.19  32665.38  35478.09\n",
       "湖南省       24621.67  27037.32  28902.21  31551.37  33902.96\n",
       "甘肃省        6330.69   6836.82   6790.32   7200.37   7459.90\n",
       "福建省       21868.49  24055.76  25979.82  28810.58  32182.09\n",
       "西藏自治区       815.67    920.83   1026.39   1151.41   1310.92\n",
       "贵州省        8086.86   9266.39  10502.56  11776.73  13540.83\n",
       "辽宁省       27213.22  28626.58  28669.02  22246.90  23409.24\n",
       "重庆市       12783.26  14262.60  15717.27  17740.59  19424.73\n",
       "陕西省       16205.45  17689.94  18021.86  19399.59  21898.81\n",
       "青海省        2122.06   2303.32   2417.05   2572.49   2624.83\n",
       "黑龙江省      14454.91  15039.38  15083.67  15386.09  15902.68"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A-3 採用分进合击初阶分析策略  pivot_table\n",
    "分进合击进阶 = df.pivot_table(index= \"地区\", columns=\"年份\",  \\\n",
    "                 values=\"地区生产总值(亿元)\", aggfunc=\"sum\") \n",
    "print (分进合击进阶.shape)\n",
    "分进合击进阶"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>年份</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>总值</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>地区</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>广东省</th>\n",
       "      <td>62474.79</td>\n",
       "      <td>67809.85</td>\n",
       "      <td>72812.55</td>\n",
       "      <td>80854.91</td>\n",
       "      <td>89705.23</td>\n",
       "      <td>373657.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>江苏省</th>\n",
       "      <td>59753.37</td>\n",
       "      <td>65088.32</td>\n",
       "      <td>70116.38</td>\n",
       "      <td>77388.28</td>\n",
       "      <td>85869.76</td>\n",
       "      <td>358216.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>山东省</th>\n",
       "      <td>55230.32</td>\n",
       "      <td>59426.59</td>\n",
       "      <td>63002.33</td>\n",
       "      <td>68024.49</td>\n",
       "      <td>72634.15</td>\n",
       "      <td>318317.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>浙江省</th>\n",
       "      <td>37756.59</td>\n",
       "      <td>40173.03</td>\n",
       "      <td>42886.49</td>\n",
       "      <td>47251.36</td>\n",
       "      <td>51768.26</td>\n",
       "      <td>219835.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>河南省</th>\n",
       "      <td>32191.30</td>\n",
       "      <td>34938.24</td>\n",
       "      <td>37002.16</td>\n",
       "      <td>40471.79</td>\n",
       "      <td>44552.83</td>\n",
       "      <td>189156.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>四川省</th>\n",
       "      <td>26392.07</td>\n",
       "      <td>28536.66</td>\n",
       "      <td>30053.10</td>\n",
       "      <td>32934.54</td>\n",
       "      <td>36980.22</td>\n",
       "      <td>154896.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>河北省</th>\n",
       "      <td>28442.95</td>\n",
       "      <td>29421.15</td>\n",
       "      <td>29806.11</td>\n",
       "      <td>32070.45</td>\n",
       "      <td>34016.32</td>\n",
       "      <td>153756.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>湖北省</th>\n",
       "      <td>24791.83</td>\n",
       "      <td>27379.22</td>\n",
       "      <td>29550.19</td>\n",
       "      <td>32665.38</td>\n",
       "      <td>35478.09</td>\n",
       "      <td>149864.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>湖南省</th>\n",
       "      <td>24621.67</td>\n",
       "      <td>27037.32</td>\n",
       "      <td>28902.21</td>\n",
       "      <td>31551.37</td>\n",
       "      <td>33902.96</td>\n",
       "      <td>146015.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>福建省</th>\n",
       "      <td>21868.49</td>\n",
       "      <td>24055.76</td>\n",
       "      <td>25979.82</td>\n",
       "      <td>28810.58</td>\n",
       "      <td>32182.09</td>\n",
       "      <td>132896.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>辽宁省</th>\n",
       "      <td>27213.22</td>\n",
       "      <td>28626.58</td>\n",
       "      <td>28669.02</td>\n",
       "      <td>22246.90</td>\n",
       "      <td>23409.24</td>\n",
       "      <td>130164.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>上海市</th>\n",
       "      <td>21818.15</td>\n",
       "      <td>23567.70</td>\n",
       "      <td>25123.45</td>\n",
       "      <td>28178.65</td>\n",
       "      <td>30632.99</td>\n",
       "      <td>129320.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京市</th>\n",
       "      <td>19800.81</td>\n",
       "      <td>21330.83</td>\n",
       "      <td>23014.59</td>\n",
       "      <td>25669.13</td>\n",
       "      <td>28014.94</td>\n",
       "      <td>117830.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>安徽省</th>\n",
       "      <td>19229.34</td>\n",
       "      <td>20848.75</td>\n",
       "      <td>22005.63</td>\n",
       "      <td>24407.62</td>\n",
       "      <td>27018.00</td>\n",
       "      <td>113509.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>陕西省</th>\n",
       "      <td>16205.45</td>\n",
       "      <td>17689.94</td>\n",
       "      <td>18021.86</td>\n",
       "      <td>19399.59</td>\n",
       "      <td>21898.81</td>\n",
       "      <td>93215.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>内蒙古自治区</th>\n",
       "      <td>16916.50</td>\n",
       "      <td>17770.19</td>\n",
       "      <td>17831.51</td>\n",
       "      <td>18128.10</td>\n",
       "      <td>16096.21</td>\n",
       "      <td>86742.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>江西省</th>\n",
       "      <td>14410.19</td>\n",
       "      <td>15714.63</td>\n",
       "      <td>16723.78</td>\n",
       "      <td>18499.00</td>\n",
       "      <td>20006.31</td>\n",
       "      <td>85353.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广西壮族自治区</th>\n",
       "      <td>14449.90</td>\n",
       "      <td>15672.89</td>\n",
       "      <td>16803.12</td>\n",
       "      <td>18317.64</td>\n",
       "      <td>18523.26</td>\n",
       "      <td>83766.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>天津市</th>\n",
       "      <td>14442.01</td>\n",
       "      <td>15726.93</td>\n",
       "      <td>16538.19</td>\n",
       "      <td>17885.39</td>\n",
       "      <td>18549.19</td>\n",
       "      <td>83141.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>重庆市</th>\n",
       "      <td>12783.26</td>\n",
       "      <td>14262.60</td>\n",
       "      <td>15717.27</td>\n",
       "      <td>17740.59</td>\n",
       "      <td>19424.73</td>\n",
       "      <td>79928.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>黑龙江省</th>\n",
       "      <td>14454.91</td>\n",
       "      <td>15039.38</td>\n",
       "      <td>15083.67</td>\n",
       "      <td>15386.09</td>\n",
       "      <td>15902.68</td>\n",
       "      <td>75866.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>吉林省</th>\n",
       "      <td>13046.40</td>\n",
       "      <td>13803.14</td>\n",
       "      <td>14063.13</td>\n",
       "      <td>14776.80</td>\n",
       "      <td>14944.53</td>\n",
       "      <td>70634.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云南省</th>\n",
       "      <td>11832.31</td>\n",
       "      <td>12814.59</td>\n",
       "      <td>13619.17</td>\n",
       "      <td>14788.42</td>\n",
       "      <td>16376.34</td>\n",
       "      <td>69430.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>山西省</th>\n",
       "      <td>12665.25</td>\n",
       "      <td>12761.49</td>\n",
       "      <td>12766.49</td>\n",
       "      <td>13050.41</td>\n",
       "      <td>15528.42</td>\n",
       "      <td>66772.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贵州省</th>\n",
       "      <td>8086.86</td>\n",
       "      <td>9266.39</td>\n",
       "      <td>10502.56</td>\n",
       "      <td>11776.73</td>\n",
       "      <td>13540.83</td>\n",
       "      <td>53173.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新疆维吾尔自治区</th>\n",
       "      <td>8443.84</td>\n",
       "      <td>9273.46</td>\n",
       "      <td>9324.80</td>\n",
       "      <td>9649.70</td>\n",
       "      <td>10881.96</td>\n",
       "      <td>47573.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>甘肃省</th>\n",
       "      <td>6330.69</td>\n",
       "      <td>6836.82</td>\n",
       "      <td>6790.32</td>\n",
       "      <td>7200.37</td>\n",
       "      <td>7459.90</td>\n",
       "      <td>34618.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>海南省</th>\n",
       "      <td>3177.56</td>\n",
       "      <td>3500.72</td>\n",
       "      <td>3702.76</td>\n",
       "      <td>4053.20</td>\n",
       "      <td>4462.54</td>\n",
       "      <td>18896.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>宁夏回族自治区</th>\n",
       "      <td>2577.57</td>\n",
       "      <td>2752.10</td>\n",
       "      <td>2911.77</td>\n",
       "      <td>3168.59</td>\n",
       "      <td>3443.56</td>\n",
       "      <td>14853.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>青海省</th>\n",
       "      <td>2122.06</td>\n",
       "      <td>2303.32</td>\n",
       "      <td>2417.05</td>\n",
       "      <td>2572.49</td>\n",
       "      <td>2624.83</td>\n",
       "      <td>12039.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西藏自治区</th>\n",
       "      <td>815.67</td>\n",
       "      <td>920.83</td>\n",
       "      <td>1026.39</td>\n",
       "      <td>1151.41</td>\n",
       "      <td>1310.92</td>\n",
       "      <td>5225.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "年份            2013      2014      2015      2016      2017         总值\n",
       "地区                                                                   \n",
       "广东省       62474.79  67809.85  72812.55  80854.91  89705.23  373657.33\n",
       "江苏省       59753.37  65088.32  70116.38  77388.28  85869.76  358216.11\n",
       "山东省       55230.32  59426.59  63002.33  68024.49  72634.15  318317.88\n",
       "浙江省       37756.59  40173.03  42886.49  47251.36  51768.26  219835.73\n",
       "河南省       32191.30  34938.24  37002.16  40471.79  44552.83  189156.32\n",
       "四川省       26392.07  28536.66  30053.10  32934.54  36980.22  154896.59\n",
       "河北省       28442.95  29421.15  29806.11  32070.45  34016.32  153756.98\n",
       "湖北省       24791.83  27379.22  29550.19  32665.38  35478.09  149864.71\n",
       "湖南省       24621.67  27037.32  28902.21  31551.37  33902.96  146015.53\n",
       "福建省       21868.49  24055.76  25979.82  28810.58  32182.09  132896.74\n",
       "辽宁省       27213.22  28626.58  28669.02  22246.90  23409.24  130164.96\n",
       "上海市       21818.15  23567.70  25123.45  28178.65  30632.99  129320.94\n",
       "北京市       19800.81  21330.83  23014.59  25669.13  28014.94  117830.30\n",
       "安徽省       19229.34  20848.75  22005.63  24407.62  27018.00  113509.34\n",
       "陕西省       16205.45  17689.94  18021.86  19399.59  21898.81   93215.65\n",
       "内蒙古自治区    16916.50  17770.19  17831.51  18128.10  16096.21   86742.51\n",
       "江西省       14410.19  15714.63  16723.78  18499.00  20006.31   85353.91\n",
       "广西壮族自治区   14449.90  15672.89  16803.12  18317.64  18523.26   83766.81\n",
       "天津市       14442.01  15726.93  16538.19  17885.39  18549.19   83141.71\n",
       "重庆市       12783.26  14262.60  15717.27  17740.59  19424.73   79928.45\n",
       "黑龙江省      14454.91  15039.38  15083.67  15386.09  15902.68   75866.73\n",
       "吉林省       13046.40  13803.14  14063.13  14776.80  14944.53   70634.00\n",
       "云南省       11832.31  12814.59  13619.17  14788.42  16376.34   69430.83\n",
       "山西省       12665.25  12761.49  12766.49  13050.41  15528.42   66772.06\n",
       "贵州省        8086.86   9266.39  10502.56  11776.73  13540.83   53173.37\n",
       "新疆维吾尔自治区   8443.84   9273.46   9324.80   9649.70  10881.96   47573.76\n",
       "甘肃省        6330.69   6836.82   6790.32   7200.37   7459.90   34618.10\n",
       "海南省        3177.56   3500.72   3702.76   4053.20   4462.54   18896.78\n",
       "宁夏回族自治区    2577.57   2752.10   2911.77   3168.59   3443.56   14853.59\n",
       "青海省        2122.06   2303.32   2417.05   2572.49   2624.83   12039.75\n",
       "西藏自治区       815.67    920.83   1026.39   1151.41   1310.92    5225.22"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A-3 pivot_table 按总值排序\n",
    "排序 = 分进合击进阶.sum(axis=1).sort_values(ascending=False).index\n",
    "排序\n",
    "排序_总值 = 分进合击进阶.sum(axis=1).sort_values(ascending=False).values\n",
    "排序_总值\n",
    "分进合击进阶_排序过 =  分进合击进阶.loc[排序]\n",
    "分进合击进阶_排序过\n",
    "分进合击进阶_排序过['总值'] = 排序_总值\n",
    "分进合击进阶_排序过"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 制表"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 地区生产总值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 使用matplotlib制图，matplotlib是Python的绘图库\n",
    "- 有关制图感谢runoob网站提供的帮助，分享：runoob网站的[有关matplotlib使用的参考文档](https://www.runoob.com/numpy/numpy-matplotlib.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1213f13c8>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TSkqKJk2apICAALlcLiUnJ2v48OGaO3fuIcc9/PDDSk9P1+DBg/X22297ZdWVFddmoMPpZ6j3hRdr9fz3tGv7NqfjAAAAAGgkioqKlJSUpHnz5qmqqqpm3BhzxK/e/PLLL5WVlaWCgoJDbiE+Gay4NhMX3Pxr/fhtij795zO68bGn5OPr63QkAAAAAHV0tJXR+lJaWqoFCxZo3LhxysvLkyRlZmbK7XbL7XZrwIABhz0uPDxckjRw4EBFR0d7JQsrrs1EUIuW+sXY8crdtlXffDTP6TgAAAAAGrgZM2bopptuUsuWLdWpUydNnz5dq1at0tSpU/Xiiy8qMjJS69atU1pampYuXaq8vDwtXLhQo0eP1ocffqguXbro6quv9koWc6Tl3YYoMTHRpqSkOB2j0bLW6v3/+6syUr/XLU8+p9C23vnXDwAAAADet2HDBvXs2dPpGPXmcNdnjFljrU2sPZcV12bEGKMhv75DxsdHi/713BHvSQcAAACAhoTi2sy0iozS+TfeovTvv9WGpUucjgMAAAAAx0RxbYbOuGSYYrr10JLZL6m0aI/TcQAAAADgqCiuzZDx8dEvx0+Ue/9+LZn9ktNxAAAAAOCoKK7NVGRcB/W/apQ2Lv9SP37LA68AAAAANFwU12bsnJHXKbx9nD7713Nyl+13Og4AAACAJqSiosJr56K4NmMuPz/9cvxEFeft1vK3Xnc6DgAAAIAm4plnntHrr7/utW8yobg2c+179FLfXw7XNx/PV/aWTU7HAQAAANBAFBcXa8yYMeratauGDRum7OxsTZgwQVOmTFFSUpIkqaioSBMmTNDs2bNrjrv77rvl6+urcePGyRjjlSwur5wFjdr5N9yiH1JW6tNZz+qmx/8hX5ef05EAAAAAOGzx4sWaOXOmgoKCFBMTo7Fjx2r06NG68cYbFRMToyFDhig5OVklJSU1K6ubNm3Se++9p23btnk1C8UVCggO1pBf36EPnnhUq+e9pwFXX+90JAAAAAAHyZk6VQc2bPTqOQN69lD0gw8e8fORI0dKkrKzs9WvXz+lpKRo0qRJCggIkMvlUnJysoYPH665c+fWHPPpp5/qrLPO0sKFC/Xkk0/q+eefV+/evU86K7cKQ5LUNbG/ug0YpJXvvqn8rB1OxwEAAADQABQVFSkpKUnz5s1TVVVVzbgx5rD7V/ft26cWLVro8ssv19lnn61Zs2Z5JQcrrqhx0bjfKH3dt/ps1kxd/6fHZXz4dw0AAACgITjaymh9KS0t1YIFCzRu3Djl5eVJkjIzM+V2u+V2uzVgwICfHZOQkKDFixdLqn6qcHR0tFey0ExQIyQ0TBfc/GtlbVyvdZ9/6nQcAAAAAA6aMWOGbrrpJrVs2VKdOnXS9OnTtWrVKk2dOlUvvviiIiMjtW7dOqWlpWnp0qXKy8vT0KFD1aFDB7300ksqKirSHXfc4ZUsxluPJz4VEhMTbUpKitMxmjRrreY++pByt23VuKdfUIvwCKcjAQAAAM3Shg0b1LNnT6dj1JvDXZ8xZo21NrH2XFZccQhjjC65/U5VVVRo8csvOh0HAAAAACiu+LmwmPYaOOpGbV29Qlu+TnY6DgAAAIBmjuKKwzpr+EhFdeysxa+8qLJ9JU7HAQAAANCMUVxxWL4uly79ze9VumePls551ek4AAAAAJoxiiuOqG3nrjpz2BX6fvHH2pOT7XQcAAAAAM0UxRVH1feSoZKk9HXfOpwEAAAAQHNFccVRhUa3U4uISGWkfu90FAAAAADNFMUVR2WMUXzvPtqx/nvZqiqn4wAAAAA4RYqLizVmzBh17dpVw4YNU3Z2tiZMmKApU6YoKSlJklRUVKQJEyZo9uzZkqQ5c+bIGFPzeu2117ySxeWVs6BJi0/oq7SvPtfujO1q07Gz03EAAAAAnAKLFy/WzJkzFRQUpJiYGI0dO1ajR4/WjTfeqJiYGA0ZMkTJyckqKSmRtVaS1L17d+3YsUOS9Oijj2r06NFeyUJxxTHF9e4jSdqx/nuKKwAAAOCApe9sVt4O735NZWRcC51/Xbcjfj5y5EhJUnZ2tvr166eUlBRNmjRJAQEBcrlcSk5O1vDhwzV37tyaYxITEyVJBQUFioqKkr+/v1eycqswjqlVZJTCYtopI3Wt01EAAAAAnEJFRUVKSkrSvHnzVHXQ1kFjTM0q6+G8/vrruv76672WgxVX1El8Ql9tWPaFqior5ePr63QcAAAAoFk52spofSktLdWCBQs0btw45eXlSZIyMzPldrvldrs1YMCAIx67du1a3XXXXV7LQnFFncT17qu1n32knB+2qF23Hk7HAQAAAFDPZsyYoQcffLDm91dffVXLli1TVlaWXnzxRUVGRmrdunVKS0uTr6+vRowYocjISGVlZaljx45ezWKOtrzb0CQmJtqUlBSnYzRLpXuL9MLtYzRo9K/U/6rrnI4DAAAANHkbNmxQz549nY5Rbw53fcaYNdbaxNpz2eOKOglu1VpRHTopI/U7p6MAAAAAaGYorqiz+IQ+ytq0QRVut9NRAAAAADQjFFfUWVzvvqosL9fOzRudjgIAAACgGaG4os5ieybI+PjwtTgAAAAATimKK+osIDhY0V1OU8Z6iisAAACAU4fiiuMSn9BXOVs3y72/1OkoAAAAAByQk5NzyO8FBQXauPF/2wnnz5+vpUuXevVvUlxxXOIT+spWVSlzw3qnowAAAAA4BVatWqUePXpo8+bNKioqUlxcnP773//WfL506VL16tVL33zzjSRp+vTp+uijj7R9+3Y98cQTXsngOtYEY4yvpBcljZSUJOlZSXdIckt6z1q7zBgzSlIfSZGS/mqtzTbGPCZpt6ROku6TFCTp75IyJO2z1j5rjOlV+1xeuSrUm5huPeTr56eM1LXqfObZTscBAAAAUI/Ky8tVXl6uLl26qKqqSitXrtQzzzyjysrKmjmbNm3S0KFDNWfOHN1zzz1au3atfvzxRy1cuFD5+fk6++yzdeGFF55UjmMWV0ntJN0r6RVJt0l6WdJkSemSPjfG9Jc0S1JbSTdJesIY84ak8621FxhjXpM0VlJXSbuttdOMMduNMZ8c5lxdrbUVJ3VFqFd+/gFq162nMtZ/73QUAAAAAPWsoqJCgwYN0gsvvFBzO3BVVZVGjRqlb7/9VmeccYZWrVqlfv36adSoUfL19VVxcbH69eunr776Ss8999xJl1apDsXVWrvDGNNZ0iOSPpQ0TlKO59VB0mhJbmut2xiTI+lSSbmez+V5v1RSZ0n/8oztkjRUUv9a5+ouiXtQG7j4hL5a/vbrKt1bpOBWrZ2OAwAAAKCeGGMkSZdddpk6duwoSTUFNjo6WuXl5Vq0aJECAwNVXFyst99+W+np6ZKkjh07Kjc31ys56rLiKlXfynuLpA0Hjdla7z/9bDyvY43VPk615lQPGDNe0nhJio+Pr2Nc1Kf4hD5a/raUmbZO3QYMcjoOAAAA0OQteXWWdqVv8+o523TorF+MHV+nuYsWLVJoaKgkKTs7Wy1btpTL5dK8efPUrVu3mnm5ubk1K6w5OTkKCgryStZjPpzJGNPKWptprc1V9f7UCkmxkqIlZUp6U1KgMSbIM7ZY0iLPHB1hrI1nbHWtc22q/fettbOstYnW2sSoqKgTvU54UdvOp8kvMEgZqdwuDAAAADRlAQEB+u6775SYmKiCggJ9+eWXmjhxotatW6fIyEhddNFF6tOnT838sLAwTZs2TdOmTVNYWJjXctRlxfUOY0wPSZ9LmilphaSJkool3Wyt3e1ZFX1YUmtJ91hrs4wxg40xd0nao+pbhIMkPWWMuU/SDGvtemPMuFrnKvfalaHe+LpciuuVoIxUvs8VAAAAOBXqujLqbU8++aTOOOMM3XzzzUpNTdX777+vrKws7dmzRzfccIPef//9Q+YXFhbqgQceqPnZW+qyx3XaYYZ/W2vO25LerjX2QK1jyiXdXmvO+trnQuMQ17uPtn2zWsX5eWoZEel0HAAAAABetnr1aj3yyCN64403VF5eLrfbrX//+9+aPn26rr32WhUUFOg///nPIcdER0friy++kFS9x/X111/X4MGDT3rbZ133uAKHiE/oK0nasf579Rp8kcNpAAAAAHhb69atNWPGDF1wwQVq06aN4uLiZK1VSEiI/P39NX/+fLVr104TJ07U3r179Yc//EEhISE1e1xDQkKUnp6uadOm6fnnnz+pLBRXnJCo+I4KbNlKGalrKa4AAABAE9StW7eaBy9t2LDhiPNefvnles9yzIczAYdjfHwU3+t0ZaR+L2vtsQ8AAAAAgBNEccUJi0voq+L83dqTm+10FAAAAKBJaqqLRMd7XRRXnLD4hOrHXu/ga3EAAAAArwsMDFR+fn6TK6/WWuXn5yswMLDOx7DHFScsLKa9WoRHKD11rfpcfJnTcQAAAIAmJTY2VpmZmdq9e7fTUbwuMDBQsbGxdZ5PccUJM8Yovncf/bj2G9mqKhkfFvABAAAAb/Hz81OnTp2cjtEg0DRwUuIS+mr/3iLlZWY4HQUAAABAE0VxxUn5aZ9rxrq1DicBAAAA0FRRXHFSWkW2UWh0jDLWU1wBAAAA1A+KK05afO++ykxLVVVlpdNRAAAAADRBFFectPjT+8q9v1S527Y6HQUAAABAE0RxxUmL63W6JCkjlduFAQAAAHgfxRUnLbh1qCLjOypj/fdORwEAAADQBFFc4RXxCX21c2OaKtxup6MAAAAAaGIorvCK+IQ+qih3K3vLRqejAAAAAGhiKK7witieCTLGh9uFAQAAAHgdxRVeERAcougupyljHQ9oAgAAAOBdFFd4TVxCH+X8sFnu/aVORwEAAADQhFBc4TXxvfuqqrJSWRvTnI4CAAAAoAmhuMJr2vXoKV+XS+l8nysAAAAAL6K4wmv8/APUrltP7UjlAU0AAAAAvIfiCq+KS+ijXenbtL94r9NRAAAAADQRFFd4VXzCGZK12pG2zukoAAAAAJoIiiu8KrrLafILCFQGtwsDAAAA8BKKK7zK1+VSbM/e2sEDmgAAAAB4CcUVXhef0FcFOzNVXJDndBQAAAAATQDFFV4Xl9BXkrRjPftcAQAAAJw8iiu8rk2HTgps0VIZ3C4MAAAAwAsorvA64+OjuN6nKyN1ray1TscBAAAA0MhRXFEv4nv3VXHebhXl5jgdBQAAAEAjR3FFvYhL6CNJyljP7cIAAAAATg7FFfUivF2sWoSFK2MdxRUAAADAyaG4ol4YYxSX0FcZ679nnysAAACAk0JxRb2J791H+/cWKX9HutNRAAAAADRiFFfUm3jP97nytTgAAAAATgbFFfWmVVQbhbaNUcb6752OAgAAAKARo7iiXsUl9NGO9etUVVnpdBQAAAAAjRTFFfUqvncfufeXatePPzgdBQAAAEAjRXFFvYrrXf19runscwUAAABwgiiuqFchoWGKjOugHexzBQAAAHCCKK6od3EJfZS1MU0V5eVORwEAAADQCFFcUe/iE85QhfuAsrdsdDoKAAAAgEaI4op6F9uzt4zxUUYqtwsDAAAAOH4UV9S7wJAWatu5i3as5wFNAAAAAI7fCRdXU22MN8Og6YpP6KvsLZvkLtvvdBQAAAAAjcwxi6sxpqUxZo4xZqsxZqExJsEYYyVVSRrumXO+MeYpY8xMY0xPz9jvjTEPGGOe85zDzxgz3RhzlzHmUc+cGGPMC8aYx4wx19bnhcJZcQl9VVVZqayNaU5HAQAAANDIuOowZ4ik30naLylbUmtJcZJKJLmNMX6S5ki6QFInSS8ZY8ZLutta28kY81dJ90vKlBRmrZ1kjPnKGHOxpNskfSLpP5KyjTGLrbWF3r1ENATtu/eUr8uljNS16nTGWU7HAQAAANCIHHPF1Vr7vqdMhkn6VlK4pJWStki6VVIPVRfZHM/rXFWX3V2eU+RIulTSJZ6ffzZmrT0gqcJzLJogv4BAxXTroYxU9rkCAAAAOD512uNqjGkt6VpJV0j6SlIHSSMkPVXrHFaSOcKYqeNY7b893hiTYoxJ2b17d13iooGK791Xu7Zv0/6SYqejAAAAAGhE6rLHNVjVJfUVSZGSrrPWVlprv5aUIWmDpCxJsZKiJaUG+vwAACAASURBVK2StEhSe88poiUt9ozFHm7MGOMvyV/VK7mHsNbOstYmWmsTo6KiTvQ60QDEJfSRrFVm2jqnowAAAABoROqyx/UuSVMP+n2bMSZC1aX119Zat+fpwnequgjfaq1d73lY04OqLruTVb1Htrcx5neSlllrPzLGfC/pz6ouub+11uZ57crQ4MR07Sa/gEBlpK7VaedwVzgAAACAujlmcbXWPi7p8WPM+VLSl7XG/nGYqXfWmpMl6fZjx0RT4OvyU/uevZWR+r3TUQAAAAA0Iif8Pa7AiYjv3UcFWTtUUljgdBQAAAAAjQTFFadUfEJfSdIOni4MAAAAoI4orjilojp2UmBIC2Ws53ZhAAAAAHVDccUp5ePjq9hep7PPFQAAAECdUVxxysWf3ld7d+dqT26O01EAAAAANAIUV5xy8b2r97lmsM8VAAAAQB1QXHHKhbePVUhYuHawzxUAAABAHVBcccoZYxTfu48yUtfKWut0HAAAAAANHMUVjohL6KPSoj3Kz8xwOgoAAACABo7iCkf8b58rtwsDAAAAODqKKxzRuk1btW4bzQOaAAAAABwTxRWOie/dR5lp61RVVel0FAAAAAANGMUVjolL6KsDpfu068dtTkcBAAAA0IBRXOGY+N59JPF9rgAAAACOjuIKx4SEhikiNp7iCgAAAOCoKK5wVHxCX2VtTFNlRbnTUQAAAAA0UBRXOCouoY8q3AeUvXmT01EAAAAANFAUVzgqrtfpMsZHGeu5XRgAAADA4VFc4ajAkBZq06mLMlK/dzoKAAAAgAaK4grHxSf0UfaWTSovK3M6CgAAAIAGiOIKx8Un9FVVZYWyNq53OgoAAACABojiCse1795LPr4uZazndmEAAAAAP0dxheP8AgMVc1p39rkCAAAAOCyKKxqE+IS+yv1xq8pKSpyOAgAAAKCBobiiQYhP6CNZqx0b1jkdBQAAAEADQ3FFgxBzWne5AgK0g9uFAQAAANRCcUWD4OvyU2yP3spIXet0FAAAAAANDMUVDUZc7z7Kz8zQntwcp6MAAAAAaEAormgwug8cJL/AIM3/x+MqLytzOg4AAACABoLiigajdZtoDf/9H7Rr+zZ9/Pw/ZKuqnI4EAAAAoAGguKJB6XLWORo8Zpw2f71cyUlvOh0HAAAAQAPgcjoAUFviiKuUn5mhle++qYjYOPU4d7DTkQAAAAA4iBVXNDjGGF18251q36OXPnl+unK2bnY6EgAAAAAHUVzRILn8/HTFPQ8qODRU7z/5mIoL8pyOBAAAAMAhFFc0WMGtQzXyj4/IvX+/PnjiMZUf4EnDAAAAQHNEcUWDFhXfUcMm3qfcH3/Qxy/MkLXW6UgAAAAATjGKKxq8ron9df4Nt2jziqVa+e5bTscBAAAAcIrxVGE0CmdfcY3yMzOUPHeOwtvHqfvAQU5HAgAAAHCKsOKKRsEYo0tu/51iuvXQx8//Q7nbtjodCQAAAMApQnFFo+Hy99eV9z6koFat9P4Tj6qksMDpSAAAAABOAYorGpWQ0DCN/MPDOrBvnz544lGVuw84HQkAAABAPaO4otFp07Gzhk68Vzk/bNEnPGkYAAAAaPIormiUTjt7oAaN/pU2JX+lr9972+k4AAAAAOoRTxVGo3XOyFHKz9qh5e+8ofDYOHXrf57TkQAAAADUA1Zc0WgZY/TL8RMVc1p3ffTc08r98QenIwEAAACoBxRXNGouf39ded8UBbZoyZOGAQAAgCbqmMXVGNPSGDPHGLPVGLPQGBNjjHnBGPOYMeZaz5zzjTFPGWNmGmN6esZ+b4x5wBjznOccfsaY6caYu4wxj3rm/OxcwPH66UnDZSXFmvfk31ThdjsdCQAAAIAX1WXFdYik30lKkDRQ0quSVkl6VNIsY0yUpDmSZkp6T9JLxpheku621k6TlC/pfkm/lhRmrZ0h6QJjzMWS/lHrXGFevDY0I207ddGwO+9V9tZN+vSfz/CkYQAAAKAJOWZxtda+b60tlBQm6VtJiZJyrLUHJFVIulFSnKQcz+tcVZfdXZ5T5Ei6VNIlnp9/NnbQuc71zmWhOTqt/7k677qbtGHZF1r1/lyn4wAAAADwkjrtcTXGtJZ0raQrah1jPa+DfzeHmWM8r7qM1f7b440xKcaYlN27d9clLpqx/ldfrx7nXaBlb72mLatXOB0HAAAAgBfUZY9rsKQRkl6RFOkZjjXG+Evyl/SWpCxJsZKiVX3r7yJJ7T1zoyUt9ozFHm7soHOtrP33rbWzrLWJ1trEqKioE7lGNCPGGP3yt79XdJfT9NGzT2nX9m1ORwIAAABwksyx9gIaYyZLmnrQ0FhJg1RdVjdYa982xlwg6SpVF+F/WmvXG2PulhSk6gI7WdJ+SdMlbZAUY619yBjTXtKfDz7X0bIkJibalJSU475IND8lhQWa8+DdMsZHY6Y+rZBQtk8DAAAADZ0xZo21NvFn443pITYUVxyP3G1b9daf7ldUx0667uGpcvn7Ox0JAAAAwFEcqbjyPa5ostp27qqhd96t7M0b9dmsZ3nSMAAAANBIUVzRpHUbMEjnjhqjtKVLtHreu07HAQAAAHACXE4HAOrbgGtGKz8zQ0vfnK3w9nHqmtjf6UgAAAAAjgMrrmjyjDG69I5JatupqxY+84R2p//odCQAAAAAx4HiimbBzz9AI/8wRQHBwXr/iUdVWrTH6UgAAAAA6ojiimajRXiErrxvikr37NEHT01VRXm505EAAAAA1AHFFc1KdNduuvSOSdq5KU2LXnqOJw0DAAAAjQDFFc1Oj3MHa8A1N2j9l4u09tOFTscBAAAAcAwUVzRL5157gzqdcZa+eO0l5f74g9NxAAAAABwFxRXNkvHx0WV33qOglq20YPo0HSgtdToSAAAAgCOguKLZCm7VWsN//0cV5ebqs5dmst8VAAAAaKAormjWYnsl6NzrxmhT8lda9/knTscBAAAAcBgUVzR7/UeOUoc+/bTklVnanf6j03EAAAAA1EJxRbNnfHw09M57FBASogXT/y532X6nIwEAAAA4CMUVkBQSGqZhE+9TQXaWFv/7BafjAAAAADgIxRXwiE/oq4HXjFbaV58r9YtFTscBAAAA4EFxBQ4y4JrRiut1uha//ILyMzOcjgMAAABAFFfgED4+vho28T75BQRq/j+mqfxAmdORAAAAgGaP4grU0iI8QsPuvEf5mRn6/JVZTscBAAAAmj2KK3AYHc84S+eMHKXUJZ9qw9IlTscBAAAAmjWKK3AE5113k9p176XP/vW8CnZmOR0HAAAAaLYorsAR+Pj6avjv/yBfl0sLpk9ThdvtdCQAAACgWaK4AkfRKjJKQ++8R7vTf9QXr/3L6TgAAABAs0RxBY6h85lnK/Hyq7X2s4XatGKp03EAAACAZofiCtTBoNG/UkzX7vr0n89qT06203EAAACAZoXiCtSBr8ul4Xf9UcbHaMGMv6uivNzpSAAAAECzQXEF6qh1m7a6dMIk5W7bqq/mvOx0HAAAAKDZoLgCx+G0sweq39DL9e1H87Vl9Qqn4wAAAADNAsUVOE6Dx9yqtp276pMXpqtoV67TcQAAAIAmj+IKHCeXn59G3HW/bJXVhzP+T5UV7HcFAAAA6hPFFTgBodEx+uVvfq/srZu07K3XnY4DAAAANGkUV+AEdR84SH0vGaaU+e9p2zernY4DAAAANFkUV+AkXPir2xTVoZM+eu5p7c3b7XQcAAAAoEmiuAInweXvrxGTHlBlRYU+fOYJVVVWOh0JAAAAaHIorsBJCm/XXpfcfqd2bkrT8nfecDoOAAAA0ORQXAEv6DnoQp1+0S+16v252v7dGqfjAAAAAE0KxRXwkl+MHa/IuA5a+NzTKinIdzoOAAAA0GRQXAEv8QsI1IhJD6j8QJkWPvukqqrY7woAAAB4A8UV8KKI2Dhd/Os7tCNtnVYkveV0HAAAAKBJoLgCXtb7giHqNfgirXzvLWWkrnU6DgAAANDoUVyBejDk1xMUHtNeC599Uvv2FDodBwAAAGjUKK5APfAPDNKIux/QgX37tHDmU+x3BQAAAE4CxRWoJ1HxHfWLceOVse47rXo/yek4AAAAQKPlcjoA0JSdftGl2rF+nZLfmaP1Xy6SX0Bg9Suw+t0/0PNzYJD8AgI8n1X/7P/TWGCQZ77n54BA+QUGyMfH1+nLAwAAAE4JiitQj4wxuuT2O9UiPEIlBfkqP1Cm8rL9cpeWen4/oPKy/So/cEAV7gPHdW6Xn79cgZ7ye3AZDgpSq6g2Co+JVXj7WIW1a68WYREyxtTTVQIAAAD164SLqzHGV9J11to3vZgHaHL8g4J1wU23HnNeVVWlKg4cUPmBA3KX7Vd5WVn168BB7wfK5K497im+bs97UW6O0td9p4oD/yvCfoFBCotpp/B2sQqLaa/wdu0V1i5W4THt5RcYWJ+XDwAAAJy0OhVXY8wQSX+y1g42xoyQNN/z0eOS3jTGjJLUR1KkpL9aa7ONMY9J2i2pk6T7JAVJ+rukDEn7rLXPGmN6SbpDklvSe9baZV68NqBR8fHxlX9QsPyDghWisJM6l62qUnFBvgp3ZqkgO7P6fWemdm7eoI3JX0nW1sxtERGp8BhPkW3XXuEx7RXePk4tIyJlfNgGDwAAAOcds7gaY3pI8pUU7xmqkGr+X3WZMSZC0ixJbSXdJOkJY8wbks631l5gjHlN0lhJXSXtttZOM8ZsN8Z8IullSZMlpUv63BjT1Vpb4b3LA5on4+OjVpFRahUZpQ59zjjks3L3Ae3J3qmCnVkq3Jmpguzq9w1Ll8i9v7Rmnss/QGHRMTWF9qcV2rB2sQoIDj7VlwQAAIBm7JjF1Vq70RhTdtBQe0mbVb1KOknSAUlua63bGJMj6VJJuZJyPPN/Guss6V+esV2Shkrq7/k8R1IHSd0lrT/JawJwFH7+AYrq0ElRHTodMm6tVWnRHhXs/GmFdocKdmZp148/aMvXybK2qmZuSGiYwtq1V3hM9R7a6luQ26l1m2j5+PLQKAAAAHjXiexx/a+qV0qvlvSCpNsO+sxKMp7XscZsrd9Va071gDHjJY2XpPj4+NofA/ASY4xCQsMUEhqmuF6nH/JZRXm5inKzVbAz07NSW30L8uavl6uspLhmno+vS6FtoxXWrr1nL21szd7aoFateUAUAAAATshxF1drbYEkGWM+VfVKabKkQGNMkKRoSYslLZL0kOeQn8a2SYr1jLXxjK32jJVJypS06TB/b5aqb0VWYmKirf05gPrn8vNTRGy8ImJ//o9HpXuLVJi9U4U7M1WYnVVdbLOztP27Naqs+N+d/wEhIdVlNqa61P50C3JodIz8AnhAFAAAAI6sLntc/VW9uhpmjLlQ1bcHfyKpXNKN1to8z6row5JaS7rHWptljBlsjLlL0h5V3yIcJOkpY8x9kmZYa9cbY8ZJmiipWNLN1tpy718igPoU3Kq1glu1VvvuPQ8Zr6qq1N7du1Xo2UP7U6HNSFuntKVLDpnbMjKqZnU2LCZW4THtFNYuVq0io3hAFAAAAGSsbTyLmImJiTYlJcXpGABOUnlZmQpzdlbvp8323Hq8M0uF2Zly799fM8/l56/Q6JiafbTdB57/s725AAAAaDqMMWustYk/G6e4AmgofnpA1E97aH9apS3cmaWiXTmqqqxU94Hna+CoGxXRPs7puAAAAPCyIxXXE3k4EwDUi4MfEBXbK+GQz/aXFGvNgv/qm4XztHnlcvU8/0INvOYGhUbHOJQWAAAApworrgAaldK9RVr1QZLWfvKhqqoq1fvCizXg6uvVKrKN09EAAABwkrhVGECTUlKQr6/fn6vvF30sY6Q+Fw/VOSNHqUVYuNPRAAAAcIIorgCapL15u7TyvbeVuuQz+br8dMalw3X2FdcouFVrp6MBAADgOFFcATRphTk7tTLpTW1Y9qVcAQE6c+gVShxxlQJbtHA6GgAAAOqI4gqgWcjP3KHkpP9o84qlCggO0VkjRurMoVcqIDjY6WgAAAA4BoorgGZl1/ZtSp77H/2QslKBLVvpnCuu0RmXDpdfQKDT0QAAAHAEFFcAzVL21k1KfmeOtq/9RsGtQ9X/quvUZ8hlcvn7Ox0NAAAAtVBcATRrmRvXa/nbryszLVUtIiI14KrrlfCLi+Xr8nM6GgAAADworgCaPWutMlLXavk7byh780a1btNWA6+9UT0HXSgfX1+n4wEAADR7FFcA8LDW6sfvUrT87Te068cfFBbTXueOulHdB54v4+PjdDwAAIBmi+IKALVYa7V19QolvzNHeTvSFRnXQedeN0Zdzx4oY4zT8QAAAJodiisAHIGtqtKmFUuVPPc/KszOUtvOXdX5zHPUMiJSLcIj1CI8Qi3DIxUQEkKhBQAAqEcUVwA4hqrKSm1Y9oW+fn+uCndm/uxzl3+AWkZEqEVYdZltERGpFmER1WOechscGiofH/bLAgAAnIgjFVeXE2EAoCHy8fVV7wuGqPcFQ1RZUa59hYUqzs9TSWF+9XtBvkoK8lVckK+sTRtUUpCvqsqKQ85hfHwUEhqmluGe1VpP0W35U9H1rOD6+Qc4dJUAAACND8UVAA7D1+WnVlFt1CqqzRHn2Koq7S/eq+KCfJUUVBfb4vzqcltSmK/8rB1KX/ed3PtLf3ZsYIuWnlXaCAWEtJCvn59cfv5y+Xtefv7yPejng999D553mHEfX19uaQYAAE0KxRUATpDx8VFw61AFtw5V205djjjPvb/UU27/9/ppJbekIF97crNV4S5XhfuAKsqr321V1YnnMj7y9feTyz9ALj+/Q8ptzGk9dObQKxQaHXPC5wcAADjVKK4AUM/8g4IV0T5YEe3j6nxMVWXlIUX2p2JbWV6uinK3KtzumvdKd63fy90q97xXuP/3cpft19rPPtK3nyzQaWcPVOLlV6ldt571eOUAAADeQXEFgAbIx9dX/kHB8g/y7nlLCvL17cfztXbRR9qyKlkx3Xro7BFXq8vZ/XmoFAAAaLB4qjAANEPusv1KXbJI3yx8X0W7chXaNkZnDrtCCRdeIr/AQKfjAQCAZoqvwwEA/ExVVaW2rlqhlAX/VfaWTQoMaaG+vxymMy4doRZh4U7HAwAAzQzFFQBwVFmbNihl/nvamrJSvr6+6nHehUocMVKR8R2djgYAAJoJiisAoE4Kc3ZqzYcfaP0Xi1ThPqCOfc/UWSOuUofTz+BrdgAAQL2iuAIAjsv+4r3VTyH+eL5Ki/YoKr6jzhpxlXqcN1i+Lj+n4wEAgCaI4goAOCEV5eXasGyJ1ix4X/mZGWoRFq4zLrtcfS8eqsAWLZyOBwAAmhCKKwDgpFhrtX3tN0qZ/54yUtfKLyBQCRddorOGXanWbaKdjgcAAJoAiisAwGt2bd+mNQv+q43JX8lWWZ12zkAlXn61Yk7r7nQ0AADQiFFcAQBeV5yfp28/nq/vF32sA6X71K57LyWOGKkuif3l4+PrdDwAANDIUFwBAPXGvb9UqUs+05qFH2jv7l0KjY5R94GDFRkXr8i4Dgpr154HOgEAgGOiuAIA6l1VZaW2rErWmoUfKGfLZllbJUny8fVVWEx7RcTGKyI2XpHxHRQRG6+w6Hby8WVlFgAAVKO4AgBOqQq3WwU7M5WfmaG8Hek170W7ciXP//b4ulwKbxeriLjqIhsZ10ERcfFq3aYttxoDANAMHam4upwIAwBo+lz+/mrTsbPadOx8yHj5gTIVZGUqb0d6TaHN2pSmjcu/POjYAIW3j1VkbLwi4jpUF9rYeLWKjJLx8TnVlwIAABxGcQUAnFJ+AYFq27mr2nbuesj4gdJS5WdmeF7pytuRoYzUtUpbuuR/xwYGKSI2rmZ19qdi2yI8QsaYU30pAADgFOFWYQBAg1ZWUqK8zHTl78g45Lbj0qI9NXNiTuuuy++erJYRkQ4mBQAAJ4s9rgCAJqV0b5Hyd6QrZ9tWrUh6U34BAbp80gOK7ZXgdDQAAHCCjlRc2SgEAGiUglu1VlzvPjr78qs15m9PKyCkheY+9pC++WieGtM/ygIAgGOjuAIAGr2I2DiN+dtT6tQvUUtenaWPn3ta5e4DTscCAABeQnEFADQJAcEhuvLeh3TuqDFKW7pEbz38x+qv3gEAAI0exRUA0GQYHx8NvPYGjfzjI9qTm603Hrxb6eu+czoWAAA4SRRXAECT0+WsczRm6j8U0jpU7/7tEa2e/x77XgEAaMQorgCAJim8XXvd+NiT6nrOAH31xsv6cMb/qbyszOlYAADgBFBcAQBNln9QsC6/e7IG3XCLNq1cpv88fJ/25GQ7HQsAABwniisAoEkzxqj/yFG65oE/qyQ/T288OEk/frfG6VgAAOA4UFwBAM1CxzPO0pjHp6tVRJTem/Znff3fd9j3CgBAI0FxBQA0G6Fto3XDo0+q+8Dzteyt1zT/6cfl3l/qdCwAAHAMdSquxpghxpivPD/3MsbMNMY8bYwZ5BkbZYx51BjzgjEmxjP2mDHmLmPMdGOMyxjT0hjzvDHmAWPMxCOdCwCA+uQXGKjhv/+DLrj519q6eqXmPHSvCnZmOh0LAAAcxTGLqzGmhyRfSfGeoZclvStppqQ3jDFtJc2S9Kik1ZKeMMZcJul8a+0MSeGSxkp6SNJua+00SfcaY7od5lwuL14bAACHZYxR4oirdO2UR/+fvfuOk+Ou7z/+mtne73bvdq/r1CxZsrpkNWPLBowNGJtiCC2EQAolBQIJIZAAIaEEiJMQ+OGQBELHgLGNC9jG3SqW1fupXb+9snvb68z8/pi9vTvdSZaNpJPkz/PxmMeUnZn97t1Dd3rf5zvfL7lkgh988qMce37rTDdLCCGEEKfxgsHVMIxDwBEARVF8wFpgoLLMAn4PKBqGUawcew3w6so2pzk2CNw8zb0WnIsPJYQQQpyNtquW8a4v3EFNQyO//PI/8uxdP8DQ9ZlulhBCCCFO8WKfcVUmbBunrMe2lWnOm+7Yqdeden/zgKL8saIo2xVF2T40NPQimyuEEEKcmb8+zO997sssuvYGNv/sR/zyX/6RQjYz080SQgghxAQvKrgahpHE7A7cAjQAPcCPAKeiKK7KsUeBRyrncJpj4cqxU+91eJr3vNMwjNWGYayur69/UR9OCCGEOBs2u4ObPvgRbnjvn3By9w5+8MmPMNLTNdPNEkIIIUSF8kJTASiKYgc+DPwDcCswBPwZkALuNwzjcUVR3gYsAwLAPxuG0asoyheBfmAO8DHABXwVM5yWDcO4Q1GUxafe60xtWb16tbF9+/aX+lmFEEKIF9RzYB/33fFFSoUCN3/wI8xfu2GmmySEEEK8bCiK8rxhGKunHL+U5rCT4CqEEOJCSI0Mc+/X/pmBo0e4+rbb2fi2d6GqlplulhBCCHHZO11wlXlchRBCiFP4QnW87TNfYskNN7Ltl3dx9xc/Sy6dmulmCSGEEC9bElyFEEKIaVhtNm78kz/n1X/0Ybr27eEHn/wIQ50nZrpZQgghxMuSBFchhBDiDJa+6ibe9pkvUC4W+eGnP8b2X91NrK+HS+lRGyGEEOJSJ8+4CiGEEGchHY/xqzu+RO+h/QB4a4O0LFpC6+KltC1eSiDSgKJMmdVNCCGEEC+CDM4khBBC/I4MwyDe30f3/j3mcmAv2cQoAL5QPa2Lx4Osvz48w60VQgghLj0SXIUQQohzzDAMYr3ddFWD7D7yqSQAgXCE1sVLK8sSfMG6GW6tEEIIcfGT4HqeHd46wMFn+ghE3NRG3NSE3dRE3PjqnFgs8iixEEK8HBi6znB3J93799C1fy89B/dSyGQAqG1sonXR0mpV1lNTO8OtFUIIIS4+pwuu1plozOVIVRW0ssHxnUPk06VJx/31LmoiZpCtCbuobXATCLtx++3yPJQQQlxGFFWlftZs6mfNZuVrb0XXNYY6T1a7Fh969kn2PPoQAMHm1kq34iW0LFqC2x+Y4dYLIYQQFy+puJ4H+UyJ0Wh28jKYZXQwh1bSq+fZnRZqImaIrW0Yr9IGwi7sTvmbghBCXG50TWPwxDGza/GBvfQe3E+pkAegrq29Wo1tvXIJTq93hlsrhBBCXHjSVfgiYOgGqXi+EmZz44F2IEsqnocJ3wpPjYOaiIuaiIea8HjF1h9yokrXYyGEuCxo5TLR4x10799L1/499B0+SLlYAEUhPGsO4dlzcLjd2F3jy8R9h8uFvbrvQlUtM/2RhBBCiN+JBNeLXLmokRgyw2w8miVRWY9GsxSy5ep5qkUhUO+irsXL8le3EZ7ln8FWCyGEOJfKpRIDRw/TvX8v3fv3EOvvpZjLUcrnzup6m8M5HmSdLhxu11mGXjcOlxuHx4PD7UG1SAAWQggxMyS4XsJy6WKlQpupVmp7O+IUMmXmrKjn6ltmE2qSLmVCCHG50nWNUj5PMZejmMtSyGYp5sylkMtSzObGt3NZitmx13LV84pZ83VD11/w/WxOFw6PB6fbUw2zjuq2t3rMOXF/wnkWqzzuIoQQ4qWRwZkuYS6vHZfXTuPc8YE7irkyux7tZtcjXRzfNcQVayKsef1sasLuGWypEEKI80FVLdVQ+LswDINyqVgNtsVcrhqCC9mMuWQyFLJpCtlsdTsdjzHS223uZzIYxpnDr9XhqIRe74TAa+47PR5sThcWqxWLzWaurTZUqxVrZT3daxbr2P74a2rlmAx0KIQQlz+puF7i8ukSO37Tyd7HetA0gys3NLL6te34gs6ZbpoQQojLkGEYlPI58pmxoJuuBt58JkMxmyE/MQBnxgNxvnL+2VR9XwzVYp0Uaqvh12rD5nTirwsTCEfw10cI1Ifx10fwh8PY7I5z2g4hhBC/O+kqfJnLJAo8/1An+5/qBeCqa5tZdVM7br99hlsmhBBCjBur+urlMlq5jFYqmetyyTxW3TePaeUyemV96mv6hHPGXpt0rFyi/BlGkQAAIABJREFUkM2SGh4kOTSIVi5Paos7UEOgPoK//pRgG47grwtjtcvvUCGEuNAkuJ5n+cOHKRw6hK21DXtbK5ZQaEa6LqVieZ67/wSHNg9gsSosvb6VFTe24fTYLnhbhBBCiIuFoeukR2MkBwdJDkVJDA2SGIySHIqSHBokOTyErk0Otp7aoBlq6yOVYBuuhltfXRirTX63CiHEuSbB9Twb+vp/Mvz1r1f3Fbcbe0sLtrZW7K1t2FpbsFdCra2pCeU8/7IbjWbZ9qsTdGyPYndYWP7qNpa9slXmhxVCCCGmoesa6VisGmQTY+uxcDs8NLmLs6LgrQ1Wg2wgHMEfjhBsaiXU3Crz8AohxEskwfU804tFSj29lLq7KHb3mOuubordXZS6ezAKhfGTVRVbY2M11NrbWrG1tJrrtjYs5/CX3Uhvmq33HufE7mGcXhsrXzOLJdc1Y7XLVAdCCCHE2dI1jXR8hOSgGWrNQDtWvY2SGh6eNGiVO1BDsLmFUHMrwbGlqQVfqE4GkxJCiDOQ4DqDDF2nPDQ8JcwWu7sodXWjxeOTzrfU1mJrbcXe2joebltbsLW1Ya2vR1HVF92G6Mkk2+49TteBGO6AndU3t7PomiYs1hd/LyGEEEJMppXLpIaHiPX1MNLbTay3u7ouZDLV82xOF8GmFkLNLZVAa65rIo0yjZAQQiDB9aKmpdOUuqav1Jb6+mBC1yTF4cDW2oJj7jz8N9+E9/rrUR1nPypiX0ecLfccp/9oAl/IyZrXzWbB2giqRQKsEEIIca4ZhkE2MVoJsj3Eerur4TY9Mlw9T7VYqIk0EmxuJdQyXqENNrdgd7pm8BMIIcSFJcH1EmWUSpT6+qZUavN79lIeHET1+fDffDOB227FtWLFWXU/MgyD7gMxtt57nMHOFDURN1ffMpt5K8MoqnRfEkIIIS6EYi5LrLdSoe3rqYbb0YG+Sc/T+kL1E7odt1TCbRtuf+AMdxdCiEuTBNfLjKFpZLduJXHPPSR/8zBGLoettZXArbcSeMMt2NvaXvgehsGJ3cNsvfc4sb4MoRYva98wh/YlMzMishBCCCFAK5cYHRiY1N14LNyWJ4yZ4QvV0zB3PpE582iYewWROfNkUCghxCVPgutlTM9kSD78MIl77iG7ZSsYBq6VKwnceiv+m2/C4vef+Xrd4Oj2KNvuO0FiKEdktp+1t86hdWHwAn0CIYQQQrwQQ9dJxYaJ9XQz3N3JwPGjRI93MDrQXz2npqGRyJz5NMydT8Oc+YTnzJWuxkKIS4oE15eJUn8/ift+ReKeeygeO4Zit+O94QYCt74B7zXXnHEaHk3TObx5gOfuP0E6XqB5QQ1r3zCXxrnSFUkIIYS4WOXTaaLHjzJw7AgDxzqIHj9KamTIfFFRCDW3TqrM1s+ajdVun9lGCyHEaUhwPc/uOnIX3zvwPcLuMBF3hLA7XF3G9kPOEBb1wkxDYxgG+X37za7E99+PFo9jCQbxv/51BG69FeeiRaftDqyVdPY/3cv2BzvJJYu0Lwmx6V0L8QTOfhAoIYQQQsyczGi8EmY7iB7vYOBYB9nEKGAOBFXX2k5k7rxKoJ1PXessGdVYCHFRkOB6nj3W9Rj3Hb+Pwewgg9lBhrJDlI3ypHMsioU6V92UYHtq2HXb3Oe0bUaxSPrpp0n88h7Sjz2GUSrhmD/P7Ep8yy3YIpFprysVNPY+3sNz95/A6bFx858uITzrzN2OhRBCCHHxMQyD1MhwNcRGjx8leqyDfCYNgMVmIzxrDpG5lW7Gc+dT29SMeoH+4C6EEGMkuF5guqETy8eIZqMMZswwG81Gq8F2bEmVUlOu9dl8pw+2HnM76AyiKi9+ChstkSD54EMk7rmH3M6doCh41q8ncNut+F71KlT31NA81J3igW/uIZcqccPvL+SKNQ0v6WsihBBCiIuHYRgkogNmF+NKkI2eOEYpnwPMOWcjs+dSP2s2Tq8Xm9OF3emsrF3YnM4pa5vThcVqlUEehRAvmQTXi1S2lK2G2LFgOzHgRrNRRnIjaIY26TqX1cWahjVsaNrANc3X0OZre9G/JIonT5K4914S99xLqbcXxe3Gf+ONBG67FffVV6Oo48E4myzy0J176T+aYOVrZrH21jmoMnWOEEIIcVnRdY14X2+1Kjtw7AjD3V3VMHs2VIulGmLtDueEwFvZdo0HYJvjlPDrcuGtqcUTDOH0eCUAC/EyJMH1EqbpGiP5kUmh9tjoMTb3baYr1QVAs7eZjU0b2di8kbWNa/HYPGd9f0PXyT3/PIl77yX54EPo6TTWxkYCt9xC4NY34Jg712xHWeepnxxh/1N9zFoS4sY/XIzdJc/DCCGEEJc7XdcoFwoU83lK+Vx1XcrnpxwrVo+b6/HX8pQKuUnnc4b/h1rtDry1QbzBEJ7K2nfqdk1QBpoS4jIjwfV8O/4EHH0E/M3gbwRfE/ibwBsBy/kLd93Jbp7pe4Zn+p5ha/9WcuUcVsXK8vByNjZvZEPTBhYGF551t2I9nyf9298yes89ZJ5+BjQN17JlNH7hCzjmzAZg3xM9PPWTDgJhF6/9wFJqIuf2mVwhhBBCXP4Mw6BcLEwKv4VMhkwiTjo2Qio2Qjo2QiYeI13ZLpeKU+7j9Pnx1QbxBEN4a0N4g6Fq4B3bdvsDk3qSCSEuXhJcz7dnvw6Pfha0U36gKip4wmaI9TeBr/GU7UrQtZ99hfR0SlqJXUO7eKbXDLKHYocACDqDbGjaUF1CrtBZ3a88NETi/vsZufO/MIpFmr/6FbzXXQdA7+E4D925D8MweM37r6J1kcz5KoQQQojzxzAMCpkM6diwGWTHAm18wnZshExidEolV7VY8dTWjgfa2hD++jBXXrMJT03tDH0iIcR0JLheCIYB2RFI9kGqH5K9kOyHVJ+5TvaZ2/nE1GsdgUqgnVCt9VeC7VjYdYfgRTzrMZwbZnPfZp7ufZrNfZuJF+IAXBm8ko3NG9nYtJFl4WXY1NPP7QpQ6uuj+8MfpnDwEPUf/Qih978fRVFIDud44Jt7iPVl2PiW+Sy9oUWeRRFCCCHEjNI1jcxofDzUVgLtxHCbjsco5rLYHE5Wvf42Vr/+TTimGaBSCHHhSXC9mBQzpwTa3krQ7RsPvekoGPrk6ywO8DWYIbamDZpWQusaiCwB65mf79ANnYOxg2Y1tvcZdg/tRjM0PDYPaxvWVrsVt/hapr8+l6P/7/6O5AMP4n/d62j8/D+iulwU82Ue/c5Bju8aYuH6Bja9YyEWm3TFEUIIIcTFLdbXyzM//T5HNj+F0+dn3RvfyrJXv1aemRVihklwvdRoZTO8jgXaSRXcfhg5ZgZfAKsTGpebIbblami92gy4Z5AqptjWv818Prb3Gfoy5r3a/e1saNrAxuaNrI6snjSnrGEYjNz5XwzdcQfOK6+k5T+/jq2xEUM3eO7+Ezx3/0ka5vi56U+W4Ak4ztuXRgghhBDiXIkeP8pTP/ounXt24qurZ8Pt72TRtdfLHLZCzBAJrpejRC/0bIPu56DnOejfNf6MbaCtEmQrYbbh9FVZwzA4mTzJs33P8nTv02wf2E5ey2NTbayMrGRTyybetuBt2Cxml+LUY4/R97GPozidtPz7v+FetQqAYzsGeeQ7B3C4bbz2A0sIz/JfkC+DEEIIIcTvqnPvLp7+0XcZONZBqKWNa37v95m7eq08BiXEBSbB9Tz7v80n+dYTx2kIOGnwO4n4nTQEHDQEXDT4zWNhvwOn7Tz+9a5cgP49lTC7zQyzyV7ztUlV2UqY9TdOe5uCVmBHdEd1kKejo0dZEV7BV6/7KvXuevOcY8fo+eCHKPb10fDpT1H71rcCMNyT4oFv7CWbKnLDuxdyxdVnrvwKIYQQQlwsDMOgY9uzPP3j7xHv66HxioVc+/Y/oGXRVTPdNCFeNiS4nmePHR7kvt19DCTyDCTzDCTyZIvalPOCHrsZav0TQm3AQcTvpLGy73dZz91f9xK9ZoDtec4Ms5Oqsq1miG292lw3LJ22KvvQiYf4+2f/Ho/Nw9c2fY0V4RUAaIkEvX/1MTJPP03tO95B5G8/gWKzkUsVeejOffR1jLLixjbW3TYXVZW/VgohhBDi0qBrGvsef4TNP/sh6dgIs5ev4pq3v4dw+5yZbpoQlz0JrheYYRikCmWilSDbn8hXt8fCbTSZZzg9zXxkNpXGgIuI32FWbwNOGv1OGgJjlVwn9V4HVstLGASpWpV9brybcbLHfM3igKblE8LseFW2I97BXz72l/Sl+/j4mo/z9oVvR1EUDE1j8GtfI/bf/4P76qtpvuNfsQaDaGWdp37awf4ne5l1VYhXv28xDtf5m89WCCGEEOJcKxUL7HroV2z75V3kM2kWbryOjW97NzUR6VEmxPkiwfUiVShrDCYLRMfCbSXY9ifHg240maekTf4+2SwKc+u9LGzwsaDBz8JGHwsbfDT4nS++WpvsG+9a3PMc9O0CrWC+5m+B9mvgVf9A0unlk099kid6nuCWObfw6fWfxmV1AZC49176P/VprHV1tHzjP3EuXAjAvid6eOonHQTCLl77gaXURGSoeSGEEEJcWvKZNM/d+3N2PHAvulZm6atuYt2bfk/mgBXiPJDgegnTdYNYtmhWaithtjue5chAikMDKfoT+eq5fqeVhY3+SqD1sbDBz4IGH17Hi6h2lgswsLcSZrfBkV+DzQ1vuhN97vV8a8+3+Oaub7IguIB/3fSv1Sl0cnv30fPhD6MlkzR94Qv4b3oNAL1H4jz0rX0YhsGN719M26LQOf36CCGEEEJcCOnYCFt+8WP2PPprrDY7q153K6tveRMOt2emmybEZUOC62UskS1xaCDJ4agZZA/1JzkSTZMulKvntAZdLIiYgXasOtse8pxdd+Ohw/DT98DQIbj247DpEzzZ9wyfeOoTKCh8+dovs7F5IwDloSF6/uzPye3aRd0HP0Ddhz+Moqokh3M88M09xPoybHjzPJa9slVG6RNCCCHEJSne38szP/k+hytzwK697XaW3/g6mQNWiHNAguvLjGEY9MRzHBpIcXggaQbagRQnhjNouvk9t1tV5oe9lcqsWZ1d2OCj3ueYGiqLWXjg47Dr+9D+Cnjzf9NtFPmLx/+Co/Gj/NmKP+N9S96HqqjoxSIDn/0siZ//Au8NN9D05S9h8Xop5ss8+t2DHN85xMJ1DVz3zgVYz+coy0IIIYQQ59GkOWBD9Wy4/R0suvYGVIv8/0aIl0qCqwAgX9I4Opjm8ECKw9EUB/uTHB5IMZgqVM8JeuwsiJhdja9s9LG0pYYrGytzsu78Adz/V+DwwZu/TbZ1DZ/d/FkeOPEAN7TewD9d80947V4MwyD+gx8S/cIXsM9up/Ub38De1oahGzz3wEme+9UJIrP93PynS/AEHDPzxRBCCCGEOAe69u3mqR99l4GjRwg2t3LN23+feavXSe8yIV4CCa7ijGKZotndeCDFof4Uh6IpjgykyJXMKX1uX9XC39+yCJ/TBtEDcNd7YOQobPpbjGs+yg8O/5ivbP8Krb5W7rj+DubWzAUgs2ULvX/xlxhA89e+inej2aX42I5BHvnOARwuKzd/YCmRdv9MfXQhhBBCiN+ZYRgc3baZp378f+YcsPMX8Ip3/AGti5bMdNOEuKRIcBUvmq4bdMWy/HR7N//viWM0Blz8y+1L2TC3Dgpp+NVHYO9PYc718Kb/Ynu6k7964q/IlXN8fuPnubH9RgCK3d30fPBDFI4dI/zXHyf4nvegKArDPSke+MZesski1797IQvWytDyQgghhLi06ZrG/ice5dm7fkA6NkL78lW8QuaAFeKsnZfgqijKuwzD+P7v1LIXQYLrzHm+M87H7trNieEM793Yzt/ctBCnVYUd/2c+++qqhbf8D9H6eXz0iY+yZ2gP773qvfz5ij/HqlrRMxn6PvEJUg8/QuC222j47GdQHQ5yqSIP3bmPvo5RVtzYxrrb5qKq0q1GCCGEEJe2UrHArl/fz7a7f0o+k6a2sYnGeQtonL+QxvkLqJ81W56FFWIa5zS4KooyDISAfmAV8PfACLDLMIyfKYryCuA2wAH8p2EYBxVF+XPADbQCnwDywL8AJ4A6wzA+/ULvK8F1ZmWLZb704CG+u7mTufUevvbW5SxrrTGnzvnpeyB+Am74FMV1H+LLz3+Fnxz+CWsb1/Lla79M0BnE0HWGv/FNhr/+dZzLltLy7/+BLRJG03Se/kkH+57spW1RkGWvbKVpfg1Wu/wwF0IIIcSlLZ9Js++3v6H38AH6jhwimxgFwOpw0DBnPo3zF1SWhXhrgzPcWiFm3rkOrm8CfgvowJ3Ar4EfYgbZBcDzwHXAbOBzwB8D9xuGMVtRlM8BKtADrDcM4z2KojwJfM4wjEfO9L4SXC8OT3cM8/Gf7WYwVeBDm+byZ6+cj62Uhvv+Avb/Aua9Gt74Le7ue5LPb/k8QVeQOzbdweK6xQAkH36Yvr/5BBaPh5av/weuZcsA2PdkL8/c1UG5pGO1qTRdUUvb4iCzFocIhF0ywIEQQgghLmmGYZAaHqKv4xD9Rw7R33GY6Ilj6Jo5haGvrp6m+QurVdnw7LlYbbYZbrUQF9Y5C66KoriA45jV07uBW4F3GIbxoKIog8A/AXdUXp8N7AP+AniXYRhrFUX5IPBeoAs4ahjG3yiK8lOg0zCMj5/pvSW4XjwSuRKfvW8/v9jRy1XNfr721uVcEfbCc9+GX38SPPXwlv9hv8fHRx77CCO5ET617lO8cf4bAcgfPkLPhz5EORql4XOfo+aNtwFQKmr0HRmla/8InftHSAzmAPDXOWlbHGLW4hDNC2qxOaQaK4QQQohLX7lYZPDkcfo7DpuBtuMQqeEhACxWK+H2uWZF9oqFNM1fiK+uXv6YLy5r5zK4WgAvZjfgzUAt8M5KcI1iBtd/wwyu7cAB4C8xw+1aRVE+ALwPM7h2VILrT4BuwzA+Ns37/TFmxZa2trZVnZ2dL6q94vx6aN8Af3f3XlKFMh+78Qred80cLAO74K4/gNFueNVniK98F3/91N+wpX8Lt19xO5+4+hPYLXbK8Ti9H/ko2S1bCL7nPYQ//jEUq3XS/RNDWbr2x+jaP0LP4Tjloo5qVWiaV0Pb4hBti4MEGz3yA1wIIYQQl410bIT+o4fp7zCXgWMdlIvm1IWemtpq1+Km+QuJzJmHzemc4RYLce6cr8GZfg74gZ8C3wWimF2FdwDXAy3AF4E/BH5tGEaLoiifBZxAJ7DRMIx3KoryBPBFwzAePNP7ScX14jScLvDJX+zlNweiXN0e5Cu3L6PNU4J7PgQH74Mrbka79T/4j0M/4L/3/TdL65by1U1fpcHTgFEuE/3Sl4l/73t4Nmyg+WtfxVJTM+37aCWdvqNmNbbrQIxYXwYAb62jWo1tWViL3WWd9nohhBBCiEuRVi4z3HWyEmQP0X/0MPH+PgAUVaW+bXb1WdmmKxZS09Akf9QXl6xzWXH9I2A1cB8QBB4FPgP0AgcNw/iJoijXAW/EfJb1W4Zh7FcU5SOAC2gG/hbIYXYpPgg0Gobxdy/03hJcL16GYfCLHb185t79aIbBp163iLevaUHZdif85lPga4Tb/5eHtTifevpTOK1OvnLdV1jTsAaA0Z//nIHPfBZrOEzgttvwbFiPa+lSlDM815GK5ashtvtgjFJeQ1UVGuYGaFscpG1xiLoWr/zgFkIIIcRlJ5tMMHD0CP0dh+jrOMzA0cMUc+YjVi2LrmLD7e+UOWTFJUnmcRUXRO9ojr/+2W6eOTrCpgX1fOnNS4kk95tdh1P98OrPcXzha/iLx/+S7lQ3H131Ud696N0oikJ2506iX/wi+b37QNdR3W7ca9bg2bAe9/r1OObPP20I1TSd6PEEnftidB0YYbg7DYDbb6+G2NYrgzg9MsCBEEIIIS4/uq4R6+nm5J6dbP/V3WTiMdquWsaG299J88JFM908Ic6aBFdxwei6wfe2dPKFBw/isFr4x9uu4g3znWbX4cMPwMLXk37tl/nU8//Co12PcnP7zXxmw2dw29wAaIkEmW3byG7eTOaZZylWnmu21NXhWb/eXDasx9bQcNo2ZBIF89nYAyN0H4hRyJZRFIjM9leejQ0RbvOhyJyxQgghhLjMlIoF9jz8ENvuuYtsYpT2ZSvZcPs7aZy/YKabJsQLkuAqLrjjQ2k++tPd7Ooe5XVLG/n8GxZTu+dOeOQz4G/GuP07/HdsB/++49+ZWzOXf7v+32jzt025T6m3l8yWLWSe3Uxm82a0WAwA++zZ1RDrvvpqLH7/tO3QdYPBk0k694/QtW+Ewa4UGOD02ph1VYj2JXW0LgrikGdjhRBCCHEZKeXz7Hr4AZ6752fkUknmrFzDhtvfSWTOvJlumhCnJcFVzIiypvOtJ49zxyNHqHHb+fKbl3K956TZdTgzBK/5Z55tXsRfP/U36LrOP7/in7mu5brTdgk2dJ1CR0clxD5L9rntGLkcqCrOJVdVKrIbcK1Yjmq3T3uPXKpI98EYnfvMKXcKmTKqqtA4v4b2JWaQrYm4z98XRQghhBDiAirmc+x86Fdsv+8X5NMp5q5ex4bb30G4fc5MN02IKSS4ihm1vy/BR3+ym8PRFG+/upW/uz6C9/4PwdGHYfEb6b3hb/nIs5/mYOwgda46VoRXsLx+OSvCK1gYWohNnf7ZVKNYJLd7N5nNm8k8u5nc3r2gaSguF+7VqytBdh2OBQtQVHXK9bqmM3AiSefeYU7uHamOVFwTcTOrEmIb5wWwWKZeK4QQQghxKSlks+x88F623383hUyG+Ws3sP4t76C+rX2mmyZElQRXMeMKZY1/fbiDO588RnOti6+8eQlr+74Hv/081M4i/6b/4r7sCXZEd7BzcCe96V4AnBYnV9VdZYbZ8HKW1S8j4AhM+x5aKkX2ueeq3YqLx44BYAkG8axbh2eD+Yysrbl52uuTwzlO7h2hc+8wPUfi6GUDu9NC2+IQ7UtCtF0VwuWdvpIrhBBCCHEpyGfS7HjgHp6//x6KuSxXrH8FG97ydkItUx/ZEuJCk+AqLhrbT8b4q7t20xXL8r6Ns/n4lTEcv3w/ZGPwmn+CpW8FZ4DB7CC7Bnexc3AnOwd3cih2CM3QAJhXM4/lYbMiu6J+BS2+lmm7F5eiUTKbN5sDPT27mfLQEAC2WW141q/He+11eNavQ3W5plxbzJfpORTn5N5hOveOkE0WQYGG2QHal5rV2GCTR6bbEUIIIcQlKZdO8fyvfsmOB++lVMizcMO1rH/L2wk2tcx008TLmARXcVHJFMp84cGDfH9LF/PDXv7tlhYWbfkYHPuteUJoPjStgOaV5rphKVkF9g3vM4Ps0E72DO4hVUqZpztD1YrsivAKrgxeic0yuXuxYRgUjx2rVmOzW7eiZ7ModjvudWvxbtqE77rrpq3GGrrBUHeKk3vMLsVDXeb7eoMO2pfU0b6kjuYFNVhtlvP7hRNCCCGEOMeyyQTbf3U3Ox+6D61Y4spXbGL9m99OTUPjTDdNvAxJcBUXpSeODPHXP9vNSLrIh6+fw4fbe7H27YC+HdC305z7FUBRof5KaF4BTWaY1cOLOJrumlSVHete7LA4qt2LV4RXTNu9WC8WyW3fTurxx0k//gSlri7z2vnz8W7ahHfTdbiWLUOxTh1tODNaoHPfCCf3DtN9MEa5qGO1q7QsDFYHePLUOM7vF08IIYQQ4hzKJkbZdu/P2f3r+9G0MouveyXr3vQ2AuHTT0EoxLkmwVVctBLZEv9w7z5+uauPRY1+Xre0kRWtNSxtrcFbGDID7FiQ7d0BOXM6HCx2iCyuBlmaVzLormXXiFmV3TW4i4Oxg9XuxXMDc8e7F4dX0OprrXbzNQyD4omTpJ94gvTjj5N9/nkol7EEAnhe8QozyF6zEUtNzZT2l0savUdG6axUY1OxPAD1bb7qAE8yZ6wQQgghLhXpeIzn7vkZux95EEPXuWrTq1n7prfirwvPdNPEy4AEV3HRe2BvP197+AhHB9MAKAosiPhY0VbDitZaVrTVMLfOg5rsnhxk+3dDIWnexOaGhqXVLsbZ8CL26Wl2Du2etnvx2sa1bGjawPqm9YTd4z+MtVSKzDPPkH7scdJPPokWj4PFgmvFcnybNuHdtAn73LlTnm81DINYX6b6XOzA8QSGAS6fjZqIG1/QibfWiS/owBt0VvYd2F1WeVZWCCGEEBeVVGyYbb+8i72P/hrDgCWvfA1r33g7vmDdTDdNXMYkuIpLRiJbYlfPKDu74uzsMtfJfBkAn8PKstYaM8y21bC8tZagywqxY+NBtm+nGWbLOfOGjgA0LTO7Fzcu56gvxK7cANsHn2dr/1ZiebOCO69mHuub1rOhaQOrIqtwWc0BmwxNI793r9ml+IknKRw8CICtubnapdh99dWojqldg3PpIl37Y/QcjJEcyZOO50nHCuj65H93NqelGmK9QSe+sXBb68RbOW6xzuyUPIZhoJV0inmNYq5MMV+mmNew2lQcbit2lxWHy4rFpkoIF0IIIS4jyeFBtt79U/Y99giKqrD0VTdx9a23460NznTTxGVIgqu4ZOm6wYmRTDXE7uoe5dBACq0S/tpDbpa31rCizazKLmzwY1d0GDo0uZvxwD7QS+ZN3SFoWonetp4jde08W46xeWAbO6I7KOpFbKqNleGV1SC7ILgAVTGDY2lggPQTT5J+/HEymzdj5PMoLheeDRvwXnct3us2YYucviuNrhvkkkVSlRCbiuVJx/Kk45XteJ5cqjT5IgXcPnulSuuoVG2deIOOahXX5bOdNjDqukGpEjSLuTKFXHk8fOYqQTQ3+fVSfuJ55nFde+GfF6pVweGy4nDbsDstlVBrmxRuq9vTHLM5LBJ8hRBCiItQYjDK1rt/wr7HH8FisbLsxtey9o1vxeXzz3TTxGVEgqvia4pTAAAgAElEQVS4rGSLZfb2JNjZPV6ZHUwVAHBYVZY0B6oV2RVtNTQGnChaEaL7x4Nsz3Yz3ALYvdC2jlzbOnYE6thcGOLZgS10xDsACDqD492KG9cT8UQA0PN5stu2kX78cVKPP065zxxMyrloEd5N1+HdtAnnVVehqC+uWlouamaQjZuhNhUrVKq1le1YnnJJn3SNxarirXXgqXGglSdXRkt57QXfU1HA7jLDo91pxe6y4HBZsTnNYGm+Zqm8VlkcFsolnUKuRDGnUciWzOCbHQ+9hezk9antntIOVam+t8NtqwZbu9sMt063edzhqazdVpxj+y4rqmVmK9NCCCHE5W50oJ8tv/gxB558DIfXy3XvfC+Lr3vli/7/jhDTkeAqLmuGYdCfyFersju7R9nbm6BYNkNSxO+oPie7oq2WJc0BXHYLpIeg8xk4+bS5DJndgM0gu57BlhVs8fjYnOvj2f7Nk7oVr2tcV+1W7La5MQyDQkcH6cefIP3EE+R27gRdxxIK4dm4AWtdParTgeJwojjsqE4nisOJ6rCjOJ0oDgeq48yvTxxMqpApk4rlq1XadMwMupnRAharWg2XDqcVWyUITgylY9tjx612FQwDo1zGKJYwSkWMUglKJYzTLeUyis2O6nGjulyoLheK29ye2N6JtJI+HmpzZYqVkFvIlsYrvGPHJoTfsTBcLpw5hNsqVV6nxwy1U8LtlP3KMZdVBtASQgghXoThrpM8/O1v0Hf4AM0LF/Oq93+QutZZM90scYmT4CpedoplnYP9yWqQ3dU9SudIFgCLqrCwwceqWbWsmlXLyrZaWmpdKJlh6Hx6QpAdq8j60FvX0tG8mM1OO8+mO3n+lG7F65rMILswuBBVUSnH42SefsbsUrxtK3oqjZHP/06fSbGbIdYMuA4UpwPVUQm9Y6HYbgddq4TP6cPm6YIo5fLv+mUfp6qVIOtCdY0HW9XtnnJMcZvHq8fcLhRX5Ry3G7XyuiUYxLDYKkG2RCFbJp8pVffzmfHjhWyZQqZEfuzcTBmtfIZqr0K1y7LDbaNhboD5qyM0zPZLoBVCCCFOw9B19j3xCE9+/38p5rKsev0bWf+m38PmdM5008QlSoKrEMBIusCu7lF2do2yo/K8bLZoVvAifkc1xK6aVcvipgD2/PB4iD35NAwfNm9k95FvW8uOyFw22+DZxFGOjJrdimsdtaxrWsf6xvWsb1pPg2d87jPDMDCKRYxCAT2fN7fzefR8AaNYmLSt5/MY1e0CRqGAUchXt/VC5fVCAb1QubZYwCgUUSwWFJtt6mK3gdV6ynH79Oeect10x7FYMIol9GwGI5dDz+XQs5V1LouRzU7Yz2HksuiZbHVfz+XMc3I5OMufRZbaWqyRCLZIBGskgrVhbLsBWySMtaEB1eudttpbLmpm2K0E2anh1zyWTRbpP5pAK+t4gw7mr4owf02Eutbp73upKOTKGJqBwyOjWAshhDi3sskET/3wO+x77GH89WFueO+fMnfV1TPdLHEJkuAqxDTKms6hgRQ7uuI832kuPXFzNGKHVWVZSw0rq1XZGkLG6OSK7PAR80YOP8Ota9hc18Jmtciz8UOM5EcAc/7YNQ1r8Dv8WBRz4CGLYkFVVHNBHd8+ZZl4/qnr6a479d7TXs9ZnHNqG6c7F+WchR/DMMzgXQm+Rq4SbqvBN4uezaIND1OKRikPRCkNmmstFptyP8XtxhY2Q6wtEsYaacAaCWNraMAajmBriGAJhc74LE4xV+bE7iE6tg/SfSCGrhsEwi7mr44wf3WEYJPnnHz288nQDQa7UnTtH6Fr/wjRE0kMwxxAyxNwmEuNA0+NfcK2A0/AjqfGgd1pnemPIIQQ4hLTc3Afj3z7G4z0dDFvzXqu/4M/xl9XP9PNEpcQCa5CnKVoMs+OzjjbK0F2f1+CUmU03Tl1nmqQXTWrlnnONGrXhGdkR8yqq+Hw09G2ks01YZ41Muwc7SCvFTC4dP69nQ1VUbGpNmyqDbvFjlW1Ylft2Cw2c105blNt2Cy2SfvV45XXTj1/ynWqnZArRIOngTpXXXWUZ71YpDw4SDkapTQwQDk6SDk6QCk6SHlgwAy4g0NTu0FbrVjD9djCkWkDrmPBAixeLwD5dIljOwfp2D5I75E4GBBq9jJ/TZh5qyIE6l0v+WtY0kvVPxKcC7lUka4DMTOsHoiRT5dAgXCbj7bFIZweG5lEgcxoobIukhktUJrm2WGb01IJtBOCbWByuPUEHFhsMhiHEEKIcVq5xPP338Pmn/0IRVHYcPs7WHHzG7BY5Q+i4oVJcBXiJcqXNPb2Jth+0gyyO7rixDJFAPxOKyvaalldCbLLa/O4+7bAyacqQfaoeRObGxQLRjmHrpfRAV0BHcXcBnTF3NYAQwENBQPQFDBQ0ADdYqdksVO22CmpdgqKnQJW8tjIGVZyukreUHGrRbxqFqeewVpOg1FGUya8V+X9NNWCYfehObwYDg+a3YNu86Lb3eh2D7rNhWZzYdjcaDYnhtVZuY+ObuhoukbZKFPSSpT0EkWtOHmtFylrZYp6kZJm7ldfH7tmwmtny6pYiXgiRNwRGjwNNHoaafA0TNr22/3jg1npOtrICKWBKOXBSsCtbkfN0BuNYuRy429iseC8ajGetevwrFuLa8UKVJeLTKLAsR2DdDw3yMDxBADhdj/zV5sh1ltrzudrGAaJQoJoNspgdtBckp0Mxo4ymOxmMD/MYDlDTDFwGgYtqpNWZz2tNXNoiyyntX4xrb5WGj2NWNXT/6LXdYPoiWS1qjrYlQIDXD4brYuCtC0K0TLPh9J5mNzOXRjlMtb6enMJm2tLTQ2lol4Js2aQHQ+2lXCbMPf18tTfGU6PbdrKrcNtxWa3YLWrWO2WyqJOOqZazl3lXgghxMUlMRjlt//7/zi+4znq29p51R99iKYrrpzpZomLnARXIc4RwzA4OZKtdC2O8XxnnCPRNACqAlc2+qsV2TWhAo3x7Si9O8z5ZqxOsLnA6qSkOkhrVlJlK0nNSqKkMlqyEiuojBQsjORhKK8SzcFARiGaB92YvrLlc1ip9dgJeuz4nFYGkwVOjmQolHXAwE2BBmuaKwMlrvAWmO3O0ezIErFkqCWJuzyKkh2B7AhkhiE/evovgCMA7iB46sAVBKsDVAuoVlAqa1WdsD32mnrG8wxFpayqlDAoAUVFMddASdEpAsOqhX5VZ0DLMZAbpD/dTzQbJZqJUjYmV1RdVpcZZt0Nk0JtxBOpHnfb3JO+r3oqZYba/n6yu3aR3bKV3J49oGkoNhuuZctwr1uH4+pVJOc30D00zMmdMUb36ehDdgwMUqEonfV72ed7hoQ1PuXLF9Q0ImWNekOl1urHpoQoGzli+gi9FOmxWihM6MJsRaHJGaQ1MJu22vlmmKUVe18t6ePQdyhBIVtGUSAyO0Db4iAtbXY8g4fJ79pJ9vkd5Pftwyie4Q8DNhvWurrxQFs/YTscrm5bgkEKeX08yFYD7uSwm0sWz/aRZRRVqYZY21jAtY0H3eqxU9a2U455ahzUNXux2i1n98ZCCCEuCMMwOLp9C7/932+RHhlmyStfwyve8Qe4vL6Zbpq4SElwFeI8SmRL7OyOs6MzzvOVeWUnDvq0pLmGkqYTyxSrS640/bQuFlWh1m0n6LER9NgJeRzUemwEPQ6CbhtBr4Og2wypIa+dGrcNh3Xqf9Z13WAgmefkSIaTw1k6RzKcGM5wciRD50i2EmpNdqvKrKCb9joP7SE3s4MO5ntLtLlz1Csp1FwMssOQjZnBNjtS2R8BrQS6BnoZDA10fcJ22dyvblfWv2uXaUUFXxMEWiDQguZvZsRTy4DDw4DVwgBl+oujRLNRBjID9Gf6Gc4NT7lNwBGgwT050DZ6Gom4I+S1PIPZQYZHujF2H8C75yQNhwZp6MujGpC3waEWhf2zFPbNUkjXNLAotpq2oaW48g0YaFjdBwh5tzAr3Icv0ERSncfeQiuPxOvYNmxHNyZXGuvtRV5bP8QSz1E8dJAunKS3GKPbYidZuAJreikNiSupy7YAkLElGK47iSMwwpx8lPbuJKGOKLauqHlDmw3XokW4Vq3CvXKFWTV2uykPDY0vg0On7A9SHhpCG53mjxeqiiUUxFpfj60+XK3YnrqotSFyeSjkSpSLOuWiNmGtUZpyTKdU0qYcm3zu+LHT/dpSVIVgk4dwm4/wLB/1bX5CLR6sNgmzQggx04r5HM/e9UN2PHAPTo+X6979PhZde4P0uhFTSHAV4gI6ddCn/X1J3HYLwUpVNOi2E/TaJwXQWrcZUn1OK+p5nn6lGmqHM5wcyXKyEmo7R8z94oRQ67CqzAq5mRXyMLvOw6yQm9khD+11Hhr8zpfWVsM4JexWQq2hTw64E4OwXoL0ICR6Jizd5jrZC9opFUW7txpsCbRQ8jcSddXQb7czoCpE9QL9uSEGsgMMZMwlWUxO29ygM0jYVUfY4qYlXWbe4RGaDo/gP57BGjc/v2rTcTcquK5oID53HXtLq+jvq4W8ShmDE1adQ3aNWMDC4rYAy1prWNZSw5KWAIlcid3do+zuHmVXT4KDfUkcJYPZJZVFqLQUVBRdRVF06lwn8ZSfw5o8gHNwAN+AFZc5yxNpJxxpVjjUonC4RSE2O0hjcBZt/jZafC20+doIu8OEnCFqnbUEHIHTPltrFIuUh4cnhdpSJdROXLSRmPk9OoWlthZLTY05nZHHYy5nsz3lNdekQbQMw0AvG5TGgmwl8CaGcgx1phjqSjHYmSKfKZnfF1Uh2Oyhvs1HuM1H/Sw/oWYJs0IIMVOGOk/w8Lf/k/4jh2hZdBWvet+HCLW0znSzxEVEgqsQ4qxMDLUnKtXZE8MZTg5n6IxNDrV2q4rTao5mrCqgKgqKwuR9KvsqKJz+PDDXE88be90cyVih1mOjwe+kIeCiMeAk4nfSGHDS4LfjLMQmh9lTt7OnVlwV8DWAv7kabrO+CANOLwNWC+5ilvrEAPUjJ7APHjSfVzYqn93qgvCVEFlMnFZOHC4wsr8P9+GDBEcHAUjYPewLzyMxfz32hquwJW1omTJWm0r70jrmr47QdlWwGqC0kk7fsVG69o3QuX+EeL+ZRst2SGmjMHSI2QPbWTRyFKdmhjK9xou71UWgLoPb2UUpUKLHbqXLXUN3sIVul49ui0JXKUU0P7XirCoqNY4aah21BF1Bah211DprCTqD1DrN7ZAzVD1e46jBok4OfEa5THkkVgmyk4OtlkiYI0NnMuY0SNnKOpOZ/DzxC1DcblSPuxpoLW4PiseNZULQtTY04ly0COeiK1G9XlKxfDXEmuskhYzZnXwszI4F2fAsH6EmrwwyJYQQF4ih6+x97Dc89YPvUMznWfOGN7H2jW/F5pC5X4UEVyHEOaDrBv3JPJ2VUNsVy1Io6eZ0NoBuGOhGZXobY+J+pVI2tl8592zOG9vXdINYpkh/Ik8qX57Sthr3WKgdC7MuGgIOGgIu87jbwF+MoiR7pw+2iR4o56d+6Np2CC+GyGIytQs5qLeyLRFgZ0+KPT2jRJMFAKyqwsJGH+u9ZdbEj9PaeQDr7h2UBwYAsITD5FbfRDS4nO6Ym3xWw+a0MHtZHcWcRs/hOOWChmpRCNeWqS92ETj+LLb9m1EMA1SV8pz59Ldcwa6aWfxGbeCobv6Ct1tUljU6eHVwiKsdXcwpH8MX34cyeLDSNRvyzhp6G69k0BcmbncSt9qIqQpxDGJ6gbiWI1ZMEi/ESRQS037/FRQCjkA13Aad42F30rHKts/uw67ap+0GZmiaOeVRZjzM6pnMhKB7mu3TvZ7JVO9ta2vDuXhRJciai6WmhtTIxDCbZLArNR5mLQqhZi/1bT6zOithVgghzrtsYpQnvv8/HHjytwTCEW74wz9lzoo1M90sMcMkuAohLhuZQpmBZJ6BRJ7+RJ5oMk9/IsdAosBAMsdAIs9weupgRG67pRpuGwJOGioV24jfSaPfSaM9Q7A0iJrqpeAMcVBvYedAmd3do+zpSXB8eDwczanzsKy1hqUtZrffRY1+nKd0PzUMg1JnJ5mt28hu3UJmy1a0WAxdUUnN38jw7Gvp1xqxWTXC5R4CJ7fiP7EVq1ZAdbtxLV+Oa+VK3KtW4lq6FNXjmXTv/kS+0r3Y7Ga8tydBpvJstd9pZXWLmxtqh1lp72J28Qiu4b2V6nOMaZ8zVm3gqafsrWPUHSTuChBzuInbncQsFuKqQtzQiBkF4qUssUKceD7OaGH0tFM9KSg4rU5cVhdOixOntbJYzGMuq2vKsbHt6nUTrp/ufIfFgREfJX/gIPn9+8kfOED+wAFKPT3VdtiamsbD7OLFZpgNhUiN5CdVZYe6UhSyp4TZWb7Kc7N+gk0eLFYJs0IIcS5179/DI9/+BrG+Huav3cD17/ljfKG6mW6WmCESXIUQLyvFsk40ORZqzZA7HnZzRJMFosk8ZX3yz0CbRaHO62AoVai+FvY5WN5aM+m51IDL9qLbZBgGhY4Oslu2ktm6ley2beipFADWcBjXqpW4V67CvWoljiuuQHmR891pusHRwfSkMHtoIIVW+RxNASftdR5Cbgst9iyNtjQNliQhJUmtPopPi+Mpx3EURrBkhyE9BJnBqc8Pj3HVgieM5qkj4akl7vQTc7iJWe3ErRbSGOT1MnnDXHJ6mbxeIqcVyevmktOK5LVCZcmTK+fRjOkHLjuToDNIu7+d2YHZ1aWdELWdoxQPHSK/3wyzxZMnq9dYw+HxIFsJtZZwmNRIgaGuSlW2EmqrYdaqUNfio3FOgIa5ARrmBKpTIAkhhHjptHKJ7ffdzZaf/xjFYmHjW9/Fiptej2qRMQlebiS4CiHEKXTdYDhTOKVya64b/M5qUG0InJ9nbgxNo3DkCKrPj6256byMrJgrahzoT7CrO8Hu7lH6RnPmyNbZIqPZ0mmv89gt1HrshNw2mtxlWu0ZGq0pGiwp6pQkNcYo/glB15obQckMQn76bsZnywDKQM5iJ2+1k7dYyVms5lq1krdYyKsqOdVcm9sqgxYrJyxwQs8T18e7fNtVO7MCs5jtN8PsHFsj7VGdYGcC/XAH+QMHKBw7Xh1gyhIMViuyZqhdhLWpidRIoVqRjZ5IMngySblkXuMLOqshtnFugFCzB9UiVVkhhHgpRqMD/PZ/vsmJXc9T3z6HV7//QzTOXzDTzRIXkARXIYQQk5Q1nUSuVJ2iKZ4tEsuUiGeLjKTH9ouT9semeTqVokCNy0a9W2G2K0erPU3AWsKhaNgVDbtqYFfK2BQdOxp2VcOKhk3RsWFuWyljU8xti1HGqujmmv/f3p3HWXKWhR7/PbWdtU/vs2QyM9kIa0JQUFAuIoIL4gYoqB+viCgqeEVFATFeELiuuK8oggtcVFRwgSu74AIqEjOEBEgymTWz9X62Wp/7x1un+3RP9yQkM9NnJs/386lPbW/Xqe6qrvM+71YZnrptvmb45Ejhlj3N3HK8gCwehv4SC57HPWHAwTDkYLXGPbUxDgYeR0gZHv94V20HV09cy3XVK3nEfI0rjydMHJqHz95FfOddkJW1rOPjVB/1yNVgNtp/FVRrzC97nDqZc/JIlxN3L9NZLPs7V3x2XjXG7msn2HXNODuvblFtfOE19BdKnhUsneqxeLLLwskOiye6LJzsEnczoqpPVAtWp0o1IKqtbasM7RukrdQC/NB7yLzSosgLsrRYHdU6T916PijImK5Sb23et9sYc/+oKp//93/lw2/5fdqLCzz26V/Lk5//3VSbze0+NXMRWOBqjDHmQeun+dmB7eD9xN2Ehc5aINxNM9JMSfOinNzyxubZ50PgCbsnqlw3lvPo+hLXVRbY651hZ36SifQk9e4xsqXDHMlWXEA7mKKIg1FIZyjGaHgR10W7uam7g4fP1dl9vE/z4En0zkOQbl5LLfU68cRuViauY7F5FYuVK1j2ptDydUOtsMd0o8/sZM6OWY/xmSp+s45XH3oNUKO+Okry8CuAzkVVXW1xnqPlRJZR5DndpZilUz0WTvVYOhOzeCZhaT5lZSlb9y7cWl0YbwnVCqQZJIn7NZNESVIl27pifpXnQVTx3FT1Cct5peoRVQPCiu/WyyA4rAbg+e739H3wPdwY5Jv0vt6QT7nPbMuG/UWh5Gm+GmzmWflu4EHAmeRkWUGelAFpmg+lGwpOExecFvfj/g0qPuMzNcZn3dSaXVtuTj3A14gZ8xCU9Lr8y1+8jU+99++otVpc/8QvZ+qKK5nas5epPVfSnJy2QqLLkAWuxhhjRoKqrgaxwwHtZutJpmTF2ctppiR5QZYXJHnBQjfl+GKP44s9ji30OLHcZ2N8MVkPuW5CeXR9mesrC+z359itp5hM76XXP8bh3gnuKbplQOtqbE8M9TMOc+WLF+rs61SoJkolVqKheRQXRElBlChhXOAnIWlwJf3KVbTrV7HSvIYsrLtjpW3Gl+6mtXw3E0t3M7ZyGL9YixCzyCOLPIpA8FWQArxyLoUihUJeUKjQq83Sre2kW99Jt76Dbn0X3foOsqC+ejwvT6j3TlHvnqLePUm9d9LNu6cI8k1G0x6+XghZUCULamR+jXxoeXV7UCPfsJ75bjkPqmR+FbZ4Z/B28SjwxU2eFARegS+KJwW+p26fp+Wy4nsFga94Ar7vtgeeDuJufE/BD+gF43S0zkocsrKirCwk5Nnazej5wth0tQxk6+sC29ZM1d4xbMwmTh68i4+9/a3c+/nPkvS6q9ujWm0okHXB7PSevYzv2IX/BY4TYUaHBa7GGGMeMrK84ORKzLGFMpgtp+HgtrOh2XMUeFzTghuayzy8usjVwRxT3Es3P8rp9ARHs0UOasyC77tXOkn5aidkdb3ABXpu+/o0FMJ4ZydTy9cw0b6WVv8aqtlOAJScVI5Q6N2Q3k2Q3E2ULOHnkPhC4bco/J2ovwPxdyLeTsTbgcc0MhQQZiyQySly7zS5P4eGC0i0jF9NCCpVgrBCGFQIowphWCUKq0RRjSiqEfghFO71VlIoSgGFllPhqjqLwo0gXZT7tJwKde85Hk47tE+LAs18itynyEPIBVHwyiDcU/DywgXouSJa4JXBuadlsJ4XSFGsX87dZw7WXe1zgVfkkLtzkcw1Kffz1DU1z1J8TV368rVcg9+N8tVe63/f9etfKPUD8h376e+4mnh8D736LN1ggo42aMchaTZUWyTQnKhsqKVdC24rtcs/I66FIlYjbbagqnQWF5g/doS5Y0eYP3aU+WNHmD9+lPb83Go6zw+Y2LWbqSuuZPrKvWvB7RV7iGr1c3yCGQUWuBpjjDElVWW5l60Gs8PzwfKplfisZql7GrCzkpDkSlzW+sZlbbB797CULVUFHWr6quX64HCD9Woh7MoDdmc+uzOfXblPUP7ciqf0vYLxTIhYC04LMvJwAS86Q1A9RRAdx68chegwuSzT84SeePQ8oStCz/PoidArB7rqeT49gR5Kjy88ELtceOJR8StU/AqRH60ub9wW+RFVv7ouTeSFVL0KkRdRldCl8SKi3KOy0idc6BAudggX2/gLK8j8Mt7CEswvonMLFPPzkOUokIYNetVZerVZes2d9Mf30K/voBtOELN+YLhKVRifimjtbNLa0SCqBQShTxB5BKFHEPluWl32ymltu+fLBWlaWRRK2s+IexlpPyfuZSTDy/215bSXkQyW+0PLvYwsLfA8wQ/dufuBO3839zbMfZcu9FbnbtnfsG1DuqGfrzZCguih00f7chZ3uywcP+oC2uNHy+D2KIsnjqNDhU7N6RkX0O7Zu67ZcWNi0u6DEWGBqzHGGPMFSLKCE0v99UHtQo9OkhEFHpXAoxL4RIFH5HtuPrRcCdbPI99fv16mHV73FRaPdzlx9xL33rVI3MtozFTxWhFp3acdwbzmnFqJ3bQcc2qlz6mVmJW+G7hqkjaTssK0LDPrrbCv2mNP1GVX0GbaW2FSVxgrlqhlCwTxAolmdEVIZdDLlHXz1RBch7dtTOMSrG7Ts9MMp9VyyhnUVAs5oAI5slp7XSCrNdf50PJge17WaOdS7gMKKY9Vbs+ARIREhHh48gOSoEI/iEj8kNgPSfyAvueRiEcsuHQUxFqQaEGsGXGREhf3o+PvOYgqjR6Md2GyDdNdj6muMNWGiQ60Osp4W6n3Qrxiin51xgW2tVl6Nbfcr0w+oObXQoHHoGl0ge8rgQeB75o9Bz74gRCEshoIer5Hmru+z2la9oVOhTRb25bl953hF5QwhDCAMNRyDmGgROX2wAeiiNyrUAQhhQTk+OTlAFirfZPLfsvrtiX5ffeB3oQfetSaIdVmSLURlssR1UZAtRmt7WuGq8vWpPvSkWcpiydOMH98rYZ27thR5o8fJe33VtNV6g2m9lzJ1BUukB3fsYux6WnGpmdpTEzaa3kuIgtcjTHGmMtYL8k5vRJzcqW/LqAdLJ9ece8uXlj3GiSlSY8ZWWG2kq5uG5ANeQShbFo7nG5dkmKTbWt10GvHYV3t8/r5IMXGbVulPXf6ipezI8rYEcXMhH2mgpgJr8eE16MpfZraoa5dqkWHSt4mTNv4aRtJVhDdvEZagRSIxSOutogrTeJKkySskOYJSZ6QFilpkZbLmVsvCwgSEVLBLSPldlb3pwhJuZ6p4PeFqAeVrlDtetS6QrUHUgRQhFBU8DTC1xC/KOca4WmIaIhIhBAhhAgREIJEqEQgIepF5F5I4UfkXkThheS+mxd+RCE+fh4TZH2CvI+f9QjyPkHWx897bvvw8iBN1icot/l5Hz+Pue/wdhOehz82hjc+jj8+jt9q4Y+P4423yvVy+3gLGWtR1FvQGEOrDfe7ZIPAtlgbiCvLyeKCfid1Uzul13bzfiel106IO9mWpxRUfGqN9cHs2nJEdeO+RogfjFY/74c6VaU9P3dWk+P5Y0foLC6sSyvi0XVTJPYAAB/ASURBVJicZGxqhrHpGZrTM4xNTbv59Cxj09M0JqasX+15YoGrMcYYY0iygtPtmFPL/dWa29PLfZb7a5n04dZyg3rYwbbhwGN129APyFkLZx/jgXig2ZU0L1jupSz3U5Z6KUu9zK33UlbirQMTUCb8mF2VlN3VhNkoYTaMmQ5iJv0eE16flvRoSpeGdqkVHYK8TyIRsQbEhPSKgG4R0sl9OoVPOwtYznyWU5+l1GM59YiJiAndpCHJYJmQwqsQVetUa1VqtQbNWpXxekSrGlKPfLpJTrvXJ+23SXpLZMk8ebJIka1QZMv4dIi8HqHXx5c+vh/jSYx4KXgJSIp6OYVkFFKghRs1OSsgVRc0Z4WQ50LuQTaozd5wIbe8NOe43lv9jKdQj6HRV8Z6wnjsMd73GIuFsT40+tDoKfW+UuvmVHo53jnujSLwyRsVtFFBx2rQqMNYHRp1vLExWjv20pzdS9gYw2s08AejfNfrUK2TehX6mU/cycrgNikD2zLIXQ14E/od1zx6K9VmSGO8QmOiQnMioj5RoTnh1gfba83Q+viOgH6nzcqZ06zMn6E9N8fK3GlW5ubK9TOszJ0hjTcMbCdCY2KSsekZxqZmaE5Pbwh0Z2hOTeEHo/N6tFFlgasxxhhjzJAsL2jHWRnQpiz3hpZXA10X5A7my/21NPn9eDVOLfQZr4WrU6sW0Bper64tj9fXb6s+yPfjxllOu5/RjjNWyvnaugvc20PbB+u9fpeiv0IRdyBuE+ZdqpJQJaFGTJWYitenQkxF+kSSUJGYUBKqXkLFT6l4KaG4yZcU39Utu0lTcoGMtebcg6A4LZtyJyL0B3Nvw/pQk+8EgVTwEyGIIewLUSzU+tDsQ7OvNPrQ7Lmgd3W97wLk+0NFoFbFa9TxG02C5hhevT70KqvG6rrWGmRRg9RvkPhVEqokGhLnAb3Up9tROksJncWY7kpyVgTv+UJ9PDoroB1Mg+1hxZqtbidVJe52XBA7vxbYtuddUDuYhpsiAy64HZ+gOTWz2gy5OTXNjquuYc8jHkVYqW7+gQ8xFrgaY4wxxpwnqko3ydeC3G5KPysYqwbrgtLoMmgemmQFvSSnk2R0k4x2nNONMzpJTjfJ6MQ5nTgr97vlwbxT7u+um2dIHlMhoUZCTWI3J6Yi6bqhzAb9rFk3d68gqvgeoS9EvkcYCKHvEXluhHDfU/ygQPwC8QokKEAy8BW8HPVy8qxLZ/4IafsUGi/h5TFFnpNkHmnuoZlQS6AWK9UEaglUU2gkwlgWUE99ailU4oKwn+Mn56rBLwUB/uQEwdQ0MjVFNr6btLWTpDZJP2zR9xv08wq9xKfbg85KStrPzzpMVPXXBbSrQW0Z6LZmqlSboQ02tM3ibtcFs2dOlwHumXXBbXt+jrjbAcAPAq54+KPYf8NN7L/hJnZccy2e99AsoLDA1RhjjDHGjIStguFempeDmPlUQzevBB7V0M0r5Tb/PDenLQrl8HyXzx4/w/F7Ps/KvZ8jmbsLP7mH8eAUtWAe319hJSiY833mfJ9532curDIX+CySQ+EC3NUgN4Fm6jGrTWaKBhOxz3gXmp2CRienvpJSbcdEy32CXrL5ifk++fQOspm95BNXkDRnSWpTxOEYfanTyyO6sUevq2c1pw8rPq2ZtXcEry3XGJuuWp/bERF3Oxz/3B0cOnALhw/cwulDBwGoNprsfcyNZSD7OMZ37nrIFERY4GqMMcYYY8wXoB1nfO7kCnfcu8Jn713kxLFDxKfvZDY9zj45xX45yXXBafbISTI6ZUDrueC21mKuPsl8pc58ENIhp1uk9DSjW2T0NKVTZBQoQaa0utDqwnh3bbk1tDzeUcZ7brm2oZmzIiTRGP3qOJ3WNL3xnfSbu0hqO0mCKXp5g0LXAlURaE5Wac2uD2gHy5V68JAJkkZNZ3GBw7fdyuEDt3Do1ltYmTsNQGt2J/tveCz7b3wcex99I/XW+Daf6YVjgasxxhhjjDEPkqpyfKnPHfcuc8eJFW4v56dPn2QvJ9kvp7jGP8UN9XmuDU6zM7uXRnxy3cjaq8cCEoGueHTLdzB3y3cwdz1vdd4TWbe/n3toLEhf8PoeQU+I+kKlJ9S60OwKkx1lonzFkqiQRC33OqXqDL3aDJ3GLL3GLHFlmtQfW3deYQityZDxHQ3Gd4/Rmq2v1dZOVfB8q629GFSVhXuPuyD2wKc4ctuB1abFO666lv033sS+G25y/WOjyjaf7fljgasxxhhjjDEXSD/NufNUezWQvePEMrffu8J8J8En3zRwfTB8T/A9IfAEX9yyL4InihefoOkdoxmepuGfYSybo5GsUIt7VPopUb8g6irNjjDRVlq9CtVsmiKYKd8VPFO+P3iGXnUK9dZGwhUKakHMWNOjNRnR2jXG+L4ZWnsmGZuu0pio4Ftge0EUec6Juz7vAtlP38Lxz95BkWf4Yciehz+KfTfcxFU3Po7Zq66+pPvHWuBqjDHGGGPMRaSqnG7H3HmqjSp4IgS+uHkZeA6CT28wL9MMAtHA8/A8CDxvNb0nnLMpr6rSjjPmOwlznYT5dsJ8J+FMJ15dnmvHdNsnyfoHITtC5J2hrnO0kiWqSYdqLyHq51S6UO9P0IxnqRTTBOoC3H5tll51mjQa2/DhBRWvT7MOYxMVxnY0Gb9yitbeKcamXI1tVLOmyOdD0u9x7PbbOHTgFg4duIUzh+8BoNocY9+jb2T/jY9j3w03MbFz1/ae6BfIAldjjDHGGGPMWQajZA8C3bl27ALecjqzvEB35U7i/mGy9Dian6IWL9CMV2h2C2qdMWrdScbiSWrZJIFOkYWTxNUp+pWJdTW2AD4p9UrOWCtkbLZOa/c4rSunaM3UaE5Zre0D1Vlc4PCn/5tDt7qmxe35OQDGd+xk/w0uiN33mBupjbW2+UzPzQJXY4wxxhhjzHnRS3LOtGPuXepzdKHL4bllTp3+DCtLtxH378HrH2Ost0it26fWrlHtTTLWn6KeTBLqFIU/CGwnz661paAaZDTHfMamqrR2tWhdMUmlGRFWfILII4h8wujsZf9Bvv/4cuH6xx7j0K2f4tCB/+bIbbeS9LpcfdMX8+xXvXa7T++cLHA1xhhjjDHGXBR5oZxeiTm60OXYYo/jZxaYP/lplpdupx8fhPg4jd4itW6PqO0TdSdp9qZpJBNU8inwysB2i1rbrSkeOb6X44vi+4ofQBhAEHqEoU9YCQgjn7AaEFQCwlpIUA2J6hFBveLmtYgg9PFDFwwHq3O3LQg8vEAumSDZ9Y/9HCBccf0jtvt0zmmrwDXYjpMxxhhjjDHGXL58T9g1XmXXeBUXgewBHrO6vyiUM52Yows9ji70OH3qJMsnP8Px5c/Q799DkXySWm+JSrdH2FGCzjh+XsXLI4I8ItAKQR7ia4inEb5GCBGeRkCESAQSkXsRhe/mqR/R9oe3heR+BeQBNkvWAtEMTzMEN3nkbi45IgWe5Hhegee5INrzwffBC8APXDAc1avUWmM0xls0p8apT7aoTDSpTDaJGhXCiv+gA2TP97ni+kc+qGNst20NXEUkBH4JOAjMqOrN23k+xhhjjDHGmAvP84QdY1V2jFX5on2TwBXA41b3qypznYRjCz2OzrdZPHmYrL8CWUye9sjSLnHeoci75HmPrOhT5D3yok2hMbkmFEUfyWIkT/DSFPIUL0+RLMfLc7y8QLICL/fw8gC/CJG8ghTh2qQBomE5RaABQgha7iNCCIAQJHTLEqLlei4BmVdBJaTwQgovKOfhJgFzASyU0xAt8IrYTcQIKZ4keH5OECphJES1kEqjQrXVpD7uguDG+BjVqTEqE02ihmtmHVX9S/Z1Rttd4/q9wKSqvkxEPioiT1fVD2zzORljjDHGGGO2kYgw06ww06zw2L0TwJUX5HNUlbxQ0lxJ8oI0L0iyotxWzrOCPM/IsoQ8S8mzlCJLKfKUPE3QPCPNYrKsT5rFpFlMXpTzvE9WLLu0WZ8ii9E0pkhjNEkhydF+Av0U6Rd4CUgW4GcBkkdIHuFpBSmqiFaBCkIVpELh1enHVTp+hbxdReeGQ7vlclqvlt3OC//wJRfkb3mhbXfg+gzgznL5BPA1gAWuxhhjjDHGmAtOytcPBT7UGL13n6oqvTSn3c9o91O63Q69lQU67TlWuidpt0/TXz5FtnyGrLNE0U6QXgZJgZd6SBrgZaELgIuIvNnf7l/pAdvuwHW4sbZuWHcJRL4f+H6Affv2XaTTMsYYY4wxxpjtJSLUo4B6FLCjVQXGgAf+XtaiuHQG5t1ouxs4f4C1ev9dwAc3JlDVN6nq41X18bOzsxf15IwxxhhjjDHmcuF5l8YoyJvZ7sD1D4BFEXkp8M+q+t5tPh9jjDHGGGOMMSNmW5sKq2oKXJq9g40xxhhjjDHGXBTbXeNqjDHGGGOMMcackwWuxhhjjDHGGGNGmgWuxhhjjDHGGGNGmgWuxhhjjDHGGGNGmgWuxhhjjDHGGGNGmgWuxhhjjDHGGGNGmgWuxhhjjDHGGGNGmgWuxhhjjDHGGGNGmgWuxhhjjDHGGGNGmgWuxhhjjDHGGGNGmgWuxhhjjDHGGGNGmgWuxhhjjDHGGGNGmgWuxhhjjDHGGGNGmgWuxhhjjDHGGGNGmgWuxhhjjDHGGGNGmqjqdp/D/SYip4FD230e5zADnNnukzAjy+4PsxW7N8xW7N4wW7F7w5yL3R9mK5fCvbFfVWc3brykAtdRJyL/qaqP3+7zMKPJ7g+zFbs3zFbs3jBbsXvDnIvdH2Yrl/K9YU2FjTHGGGOMMcaMNAtcjTHGGGOMMcaMNAtcz683bfcJmJFm94fZit0bZit2b5it2L1hzsXuD7OVS/besD6uxhhjjDHGGGNGmtW4GnMRiciW/3Pn2mcuDSJyg4g0L8Bx7d4wxhhjzEOaZYbOExF5pYjceD/S2d/8EiYisyLyMhHZKyJXbph+WkR+aSjtX4vIa0QkEpHnicg08GIR+bSI1IbSPUZErgH+t4gcKj/jWSKyZzt+R/Og7AYOisgTReRbReS55XSziLxXRKZEZLeI+CJSF5EPishbAESkKiI7hw9m98boEhHZ7nM4FxG5QkS+T0QmtvtczAMnIl8iIj8lIvvP1z1n+ZDRtCFf4J+nY+4Wkd8WkS8/H8fbcGx7xowQKZ3H443k9bWH1xZEZJ+ITJbLXyUih0Xk+jLD+VoRCYfSPhN4AzBbrouIvN6Ck8tSBfhV4KuA9wC/AfwX8AJgGvi+oeBjHBAgAt6Buz8UaKpqD1YzEG8H3gbUgbRMvwu4Q0ReeVF+K3O+fAL3ruk7VPUvVfWdwN8DO4FPAg8Hfgn438AzgQ8Cp0TkubhnyAEReQLYvXEJeI+IvG94Q/m98XgR+SYRyUTkSSLyKyLynyLyLSLybyJy5cYDicj7ReR3zvVhIvIVInJARB5Xrvsb9j9JRD4kInvKzMs3Ar8OfOWGdG8vz+MjG6YjIvLHcgFaDJj1ROTVIvKJ8u/+MRH5p3L5sIh8w4bkNwE/CywCHxWRF2041rNF5ODGvIS4wtWf27DN8iHnWZknfK+I/GD5P/oMEblaRF5c/l//SHltP13+/75yaPplEfmFocDgJSLyKREZA36oXP5dEfnRoc+7YpP/3eH/4RdsOMUnAj/AUH5fXCHpEzb5XewZMwJE5EfPcY0/Wv7vfk2Zdp+IrJTX6logK+/J94jI1RuOe1lcXwtct/YM4IiIfAS4GZcZfRMuY/ozwDvKG+gjwB8BK8DN5fq/Ai8Ffg4sA3qZehdwGnetu8BHcIHJGVU9WaZRIBn6mfYmx3kD8Gbgy8vj1IAPAC/BBchnZXLNSOsC36iqi7BaK7cM/I2q/jRwF/Bp4NdV9Z2q+n9U9RVlgHsa+GPgzvJYdm+MmPKLPRqsUl4rcbVi1+OuUwbM457z9+K+G04Cx3DPjbnyvlh3aKAoj7WjzDzsLNefISKfxWUgBPhVEfkP4DYRuWlwAFX9N6CFKxBpAT8IPAdoSlkIW9oBvB94mqo+VVWfCnwY+DPgxaq62XPKnF9juGfBISAE3o3Lj80CCxvSTgH3qOoS8FbgZJmxDQFU9a9x987XlJnJW0XkDPDnwNeJyFPA8iEXgoi8FOgD+4CfwP1tXwH8AnALLmB8F/CtwC8DB3CtcnJcQDkF7Bt8XwBfDbwTeHG5/zbgb4ArReSXRERU9Tjw9PL/9iCwUC4/V1X3qupbN5zm1+GeS68bBBjAx4F/FlfpYs+Y0fNbQ383cNfrKuDtqvoUVd0PNMpr+edAE1cI/jbcc+RO3PX5drj8rm9wIQ9+icuBztCNsykR+TLcl85zgKcDv6Gq925INsiA/iaupmWQAe1jGdBLWQM4co79AdDbaqeITOEeJFXgCbgHzseBt+CCnU+oanzeztZcUOJqSl4P7BORJ6nqHaqqZQYzL5PN4Uovv/bs2IWrcTWzf1IGNnZvjJ7fBMZF5BhwI/DIMvOwG7gCeDLwB7j/+//GBRoBrvDh0cB+4AwQisjzh4772PJYjxna9mJcTZsC1wO+qg6C2+uAz+OCHkTkBuBFuJYg34TLPP88cDvwJcBnReSpqvqZ8th94IUisoLLGL8I+DtV7T/4P5G5n04B34Ir2HgCLvD5IICIfDXwU2W6q4GJ8j4D90z4UuBhIvIK4GnA44DH4O6/7wJeo6pftuHzLB9ynqnqb4nIlwIfxV2TAPf3nFPVT5S1Yk3ctfw4rtDyelwweTVwlLVr/ohyvw/8KK4Q427gWbjvjSO4744PAd9cBs1fjCvI+Ahwk4i8U1VXa+RFZBb4hvLcurgA+ndxAfKX4L5LwJ4xI0VV0/Le+Rgu0JwvdyUi8hJcAfffAv8G/BCuIOq5uGDzY8Cf4p4Jg0LWy+r6WuC6NR9YOlcCEZnBfRE8B/cl9Erg98sb7npV/U0LTi5bPi6j+mFcUPJIYGZDmgrnuIdUdV5cc/IfB34E+E9cDe3LcRnZA8BTzvuZmwvlPbgvgtuBz26WQFVzEXmWqqYAIvJ64BtU9bEb09q9MbLeoKpvLTOLf6eqbxTXfPONuMzmvwO/z1qmAeCGct9O4E/K6/97g50i8gFc8/KXnuNzPzRU2DHohrICoKoHROSby89+CvC9uBq9TwD/gctkPA8XsAAUqvqHZSbodtwzzGrcLh4Pl9FcwBVmjOEKP6q4a/EhVX0fgIj8Ce46Pwd3DReAx6jq28v934ZrIrioqv+6SYGYFZJeIOKa8D4ReBQwgauJyoB5EXkirlDhNbgA9R5cnsDDfVc8GheMDrqd/QzwMFze4reBV+MChxcAtwJHVfVDAKr6ThH5e9y1e7aq3ioi7wcOicjjgVtUNQNeC/xP3P/4lwO/iCu4+Meypn4je8aMjptxrSEG3xu7cH+/XcDX4Grz317u/y9cAPswXEuKf8IFqu/YcMzL4vpa4Lq1kLVSjq18I6407WdxJR7gbpQEUBF5t6oetgzoZem7gf+lqqdE5DeAr8eVdg2bAg5vdQARuQLXf2kB16TwJ4d2/zLwufN6xuaCKoPSwbLC+sE2htKlGzZtXLd749KwF9glIl9UrivuWt2I6/v+KFym4j9xX+rX42phr8BlMJvAP5Q/N6hxvQGXwX2dqt7JkOHWP0Ol5cMehsuo3oxrnvgPZY3/Tbj75T82+R3exVqt7rXAp77QP4J5QI7irtXngd/B1cz/Oy5IeULZZG/gelzQ+Rxc7cvv4WpgP66qd+NqWH8S121lU1ZIesH8pqr+qoi8FdfsexlXSPU5XK3mK3DP60/i8pMvxz3TB12Ingm0ROSNuP/DHwA6uPExPsVaoPJw3LPmz3EFHftwY2rcCtwobmDQAhc8/zzwPhH5U9w99lJcsDr4DrkSV2P7ZlVd1yzdnjEj5/XA64DvBP6KtYKMf8cVQu3HxR7X4Aq/Poa7d64DvhkXfK66XK6vBa5bm8Y109iSqv6RiHwKV4L5SdyXwSvcLv0oWAb0MvZu4JiIVHG1K7+P60/wagARqeMeIP/CWl/ydX3Ky74qx8vS8FncQ2rgelyzIXMJEjfowZW4TGaOy6jeb3ZvjLayWV8bV5tyF675Hqp6m4g8HXgZrgatD3wP7nshAF6uqoeGDvUU4NoyAEFErsL1W3sTa32dB5/5kaHV2oZ9TVzzrq9Q1VeJyLuBz4jIHC4Ts4Kr4Rn+Gb/8nO/C9WX6gIg8aqiPvrkAym4Af4KrmQN3H30Ol3/4NPB6Eamqar+8rtfhBnj7VdzAbgHuOp8uW329AXiSqv78OT7T8iEXxg+LyDcBj8B1DziEu16DgocCV5B1NS6QuBNX6/ofuOa77wP2lzWmz8P9D78W11fxw0OfU8M19Q1U9aNlIPG7uHtn0DS4igsevkVVVwY/WNai3cxaRcxRXE3vgbLp551DaT+y4TMZ2mfPmIvvS3G1458UkUXWCjI+gPvuuAp333Vwz4fX4ArCvwP4sKp2hg92uVxfC1y39lhcafh9eR1u0I1PluuHgBeIyGtxTQAtA3p5CVhrGnz1hn3X4PqigAtiX6aq3TKI/QPWvjg2G+b+8IbSsP93Pk/aXHgishv35fEXuOv9O8CrcJnNqTJjsqCq7xM3UMoX474Q9D4ObffGaKnjMovPUtWOiLwXV/uKuP7M3wZM4ppYPRKXyfgt4FWqulkBRrHJtnzjhnOVlqtqu6x1OTHYVH7e34vIzwNPHPRtKgW4fnevLJuIHcQNBPNYXGbaXDg34/otdsv1Lq6g40dxQc6twPNxtbAvwDUb7eGu2R24vs/7RaSrqmdEJOc+ajksH3JhlLWtL8YNvnYY+GfcNfwE5cA4uP/JHBdkhriA4Tm4WtPjrBVQvRvX3/WRuCbGsFbD9i5V/bWhj67imihfr6qfBxA3mvBbKAvPy23PxAU+fykiTx76+XcCv4K771a7J9gzZuQsquqryuWn47qj/VG5/sfAC3GB7Glc0HoCV8j1Z7jB2m5U1VsHB7tcrq8Frpsoa9Ei4P/eR7oX4KrJ3wEMBkJQ3GjCP44rEXnP0I9YBvTSdwiYUtXlTfZ9ADey9C7ctX4/gKp2ge8HEJEU91qDYT6wY0Np2CNxGRhz6QhxNWzPxzUT/W1cv6LBc7bABaqoalGWZP4PXOn6VuzeGD0t4CcGzcFV9XtE5AdxBRERrr/RCq4W5k245lvvBt4lIrcDv7OhKeg7RGQwoEV1qw89V2l5qY/rCzXwcyLyclyB2nDroSquNvjHBv0aVfV2cf10XyYi/2T9HS+oX8AFLa/AjRD+DuCHcU0BJ3GZwb8UkWtxmcA34lpu1HD3UgNXK/eTuJqWMdb6Sa4jIn8F/JSqDve5t3zIeSIij8S1tmjg/sdeB/yVqt4y1Jfwmbgg0Qf24FrzvR/X1exjrHUx+iFc7flrcLXgNw99lCcib8YVhq8MbT+rm8ngf1fcK3Z2q+rLyxrar1xLonn5/76uZsyeMSPnW0TkSRu2PQoXV1yHa9G3Gxe8Xo97Xjy9TPcyXHyymle4XK6vBa6bexrwQ6qabJWgLLl8v6oeEzdM9BtwJWcnVTUWkWtV9dTQj1gG9DJQZlY3C1qH05xgrdRq47434wb0Gt72x7jSM3MJU9XDuFFCwZU8PhVWm9PohtJKyuah53x/ot0bIycFDgyCVgBxo4q+GpcZBVcj+z7cAF3vwo24+GlchuL5uNoYcF0H2sBzVPVYeax9uO+E4dcJVID2hmDjWlxzxN6GdIMWHz3gJWWzwtfjMjWDV6IcwRWY/KNs/q76b8WV2JsL49tx3YuWcLWfT8X1ff5OXB/HX8TV2u3CtdzYiyv4zHF9p3+v/Nnn4gKcHrAoIt+P6zsZAL9YFqx/GNcPbhC4Wj7k/GoCv4YbDOfVuJrUQRPfFFguu5Q9GfhrXMuLF+IKGw7iCrd2iHtl0d8BH1HV/wIQEcVVgMzhglxw1/wt5XIGvK0sDAcXcHRExCu/a9plfgNcU/DnAR8cCjTeO/R72DNm9CS4wHC48gsR+Qdc/vIRuIqU1+L+r6/HNS0Ocf/Pv4EbIfhNXGbXV4a+f40xxhgzQkRE1L6ojTEXiD1jLm+X2/W1wNUYY4wxxhhjzEjz7juJMcYYY4wxxhizfSxwNcYYY4wxxhgz0mxwJmOMMWYbiMjNwLyq/na5/mzcyKFPGUrzdtyrtzaO0ngtbkTTl6hqG2OMMeYyZzWuxhhjzPbYD+wWkT0i8jHc63QKEQnK0RsBduBGLH6aqj61HBnyw7hRG19sQasxxpiHCgtcjTHGmItM1t4jcB3wHcCTce/Ra+JeZXCniIyXafrAC0XkeSISAS8CxlW1jzHGGPMQYYGrMcYYc/G9Gfh64EnAs8ttT8YFrv8K1FR1qdxeqOofAjPA7bh3er7y4p6uMcYYs70scDXGGGMuMlV9IfAPwNuAPy03H8C9XH4ZV8u60buACVxwe+1FOE1jjDFmZFjgaowxxmyfAvjbcnkJF5R+K3B8OJGI+MCbgO8C/g34gIjsvIjnaYwxxmwrG1XYGGOMuchEpAK0cDWnd5abfaAHvJf1BcsB8FPAK1X1gIgcBG4DHgu876KdtDHGGLONLHA1xhhjLr7fw/VvfR5wV7ntTuB7cDWvbxKRjwNVYDfwY6oaA6jq7SLyIuBlIvJPg+3GGGPM5UxUdbvPwRhjjHlIEZGrgbaqni7X3wM8BQhxgy/N42pZvw54dLm+mT9U1T+78GdsjDHGbC8LXI0xxhhjjDHGjDQbnMkYY4wxxhhjzEizwNUYY4wxxhhjzEizwNUYY4wxxhhjzEizwNUYY4wxxhhjzEizwNUYY4wxxhhjzEizwNUYY4wxxhhjzEj7/4DTI7a7HucBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.sans-serif'] = ['Songti SC']\n",
    "分进合击进阶_排序过.plot(figsize=(16,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x121646320>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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L+hdACjgB/FugDfw08DdAzvf9H97TdyIiMsG6Xg9vpxeun+lWKtDv09vcJJWZAaClSupNGVZSrwupVX1+IiIib4SbuZP6HcALvu8/ASwDTwHPYcLqBy3LygO/BjwBfBj4Bcuy3g78kO/7P4kJs+8FvgeY9X3/ceAbLMv6e3v9ZkREJtWgShpWUisV82upRCIVw4pYave9SW7dIxKxiDvm57iDkKrPT0RE5I1xMyH1t4B3WJZ1EvgV4E5gw/f9HaALPIiplm4E/3wtcDdQDF6/AXwz8PeD348+totlWf/MsqxLlmVdKpVKr/tNiYhMmsGwHycdx+/16G1uAiakWhELJ22r3fcmuY0OybQdrvIZtEtrDY2IiMgb42ZCagb4JaAOvO+61/jBP6NfWzd4jhX8c/1ju/i+/0Hf9+/0ff/OfD5/U29ARESGVb5kxqa3vQ19sxO1G/zAz8nEVQm8Se3rVvlEYxESUzG1+4qIiLxBbiakngHmfd/fAj4HTAErlmXFgTjwG8AasAIsYVqB/wg4Hrx+CTgfPLZy3WMiIrIHwnuUaZtuuRI+PgyptkLqTXLrHsn07n2yqWxCd3pFRETeIDcz3fcp4F9YlpXGtPC+BTNI6Tjw/b7vFy3Legh4Dyb0vtv3/U9blvUzlmU9CuSAs4ALfKVlWT8AXPB9//f3/u2IiEymQQB1MnF6XyyHj49WUgsv1Q7kbEeN2+iQP5nZ9VgqG1clVURE5EvodrvEYre+QOZLfgff9z8BDOYO/0bw6/dd95yPAh+97rH/eINv957XcUYREfkS3IaHFbFIODFqwdAkKx7fVUltqxJ4U9oND2fK3vVYKquQLyIi8mp+9md/lkwmw8MPPxzOdXi9bqbdV0REDrl2PRj2E7HCdt/EW95CtzispHbaPbpe7yCPeej1en12Wl2SmevbfeManCQiImOvXq/z0EMPcdttt3HPPfewvr7OmTNneOyxxzh37hwA1WqVM2fO8PTTT4ev+6Ef+iGi0SiPPPLILQdUuLl2XxEROeTchoeTNtW/XqWMZdvE3/xm3E98AoBUZrBGxSMzFz2wcx527ZG7vaNS2TjdnR6ddpd4Uv/pFBGR/bfx4z/Ozouf2dPvmXjbV7D06KOv+Ofnz5/niSeewHEclpeXefjhh3nggQd48MEHWV5e5u677+bixYs0Gg1838zP/exnP8uHP/xhvvjFL+7ZOVVJFREZA259OJG2W64QzeWI5fN0y2V83w//TMOTXl0YUm9QSQV9fiIiMt7uv/9+Zmdn2dra4o477uDSpUssLS2RSCSIxWJcvHiRe++9l2h0+APvP/zDP+Srv/qr+e///b/zDd/wDXz605++5XPox8EiImPAbXTInzDDfrqVCrH5eWL5PH6nQ79WC0OXdqW+unCVzw0qqQCtaofpfOoNP5eIiEyeV6t47qdqtcq5c+d45plnOHHiRPi4ZVlh9XRUs9kknU7zrd/6rXz0ox/lgx/8II8//vgtnUGVVBGRMdAeafftVsphSAUz4XcYUlUJfDXuK7T7OoOQqnupIiIyxlqtFs8++yyPPPII5bLZFrC6ukqn06HT6XDXXXe97DXveMc7WF9fB8x036WlpVs+hyqpIiJH3PXDfnrlCsm3vW13SF05DaBdn1/Cl2r3VUgVEZFx9vjjj/PoSAX3qaee4sKFC6ytrfHkk0+Sy+V4/vnneeGFF4hGo9x33318y7d8C7/zO7/DL/zCL1CtVnn/+99/y+dQSBUROeJGh/34/T7dzU1i87ldITWViBK1I2r3/RLCdt+pGPU//mNKP/tzvOk3fwMnE8eyFFJFRGS8nT17lrNnz+567F3veteur2+//Xaee+65XY/94i/+IgDf9327NpW+bmr3FRE54gbB08nE6ddq0O0Sy80TWxiGVMuytCv1JrgNj0QqRiQawf3L/4+dF1/EK5aIRCySaVshVURE5A2gkCoicsS5DROcnLRNt2J2pEbn54lMTWE5TrgrNZWJ01Il9VW1G17Y6jv4LLvFAgCpbEIhVURE5A2gkCoicsS1g+CZzNh0yyZYxeZzWJZl1tCUTEh1MnENTvoS3EZn1wAqgG6xCEBqOq6QKiIi8gZQSBUROeKGldQ4vSBYxXLzwa+5kZBqK6R+CW7dC9fP9ILA3y0EldRMnFZt58DOJiIiMikUUkVEjji37oFldnsOKqnR+SCkvqyS6t1wx5kY7g3afb1BJTUbx63p8xMREdlvCqkiIkec2/BIpmwiEcsEq1iM6PQ08PKQ2uv28dq9gzzuoeX3fdoNU0n1fZ9eeCc1uNM7bT6/jts9yGOKiIiMPYVUEZEjrl3v4GSG9yhjc3NYEfN/77F8nn6zSb/VCp+jXak3tuN28fs+TtqmX6vhe+au76Ddd1Bh1b1UERGR/aWQKiJyxLmN3fcoo8F9VGC4K7VcDkOWdqXeWLhvNhMPW32JRncNTgKFVBERGV/1ep2HHnqI2267jXvuuYf19XXOnDnDY489xrlz5wCoVqucOXOGp59+GoBf+7Vfw7Ks8J8PfehDt3yO2C1/BxEROVBuvcPs8hRg7lHG5nPhn4UhtVQilZ8Nny8vN/hcnLRNt2wGUCXe/GY6a2v4vk9KlVQREXkD/Y/f+mvKVxt7+j1zJ9J8/Xd8+Sv++fnz53niiSdwHIfl5WUefvhhHnjgAR588EGWl5e5++67uXjxIo1GI5zR8Na3vpWrV68C8IEPfIAHHnjgls+pSqqIyBHnNrxda1Ni8yOV1IVhSB20+yqk3pgbVFKTaTu8j5p8+9vxWy36zaYqqSIiMvbuv/9+Zmdn2dra4o477uDSpUssLS2RSCSIxWJcvHiRe++9l2g0Gr7mzjvvZGVlhVQqRT6fJx6P3/I5VEkVETnC+n2fdtNMpPV9n165Eq6fgZFKarHEdFrtvq9mtN3XKw9C6tuo/rf/RrdQIPmmNxOJWAqpIiLyhni1iud+qlarnDt3jmeeeYYTJ06Ej1uW9aoT7n/lV36F7/zO79yTM6iSKiJyhO00PfBN9a/faOB3OkRH2n2jMzMQi9EtlYjaEeJOTJXUVzDcN2vTrZQhEiHx1rcC0C0WsSIWTsZWSBURkbHVarV49tlneeSRRygHV19WV1fpdDp0Oh3uuuuuV3ztpz71KW6//fY9OYcqqSIiR9igKupkhvcoRyupViRCLJcbWUNjK6S+ArfuEUtEicWj9CoVonNz2EtLAHjBhN/UdAJXIVVERMbU448/zqOPPhp+/dRTT3HhwgXW1tZ48sknyeVyPP/887zwwgtEo1Huu+8+crkca2trnD59es/OoZAqInKEDat/cXqVVYBdd1KBXSE1lYnTUrvvDbmNDs5UcLe3XCE2P09sYcF8PdiVmo2rkioiImPr7NmznD17dtdj73rXu3Z9ffvtt/Pcc8/teuz48eP8yI/8yJ6dQ+2+IiJH2O5KqrlHGb0+pObzYUhNplVJfSXthjeyb9aE1EgqRSSTCdfQONk4rerOQR5TRERk7CmkiogcYe2RSmq3ErT7vkpIdbJxhdRX4NY9ksFwqV65HO6bjS0s0B20+2bjuHUPv//KgyNERETk1iikiogcYS9bmxKJEJ2d3fWcWD5Pb2sL3/NIZeK0GwpZN+I2OjgZG9/3d+2bjS3kw0pqKhs3E5VbapkWEZH98WoTdI+q1/qeFFJFRI4wt+4Rd2JEYxG65QrR2Vmskd1lMLKGplIJQhgKWTfQrpt9s/1mE39nJxxAZS8s4o2EVIBWVdVoERHZe8lkkkqlMlZB1fd9KpUKyWTypl+jwUkiIkeY2+jgpHffo7xebCEIqaUSTsYMAnJrHk761pdtjwuv06Pr9U1FOpiSPLjbG1tYoFsq4ff7w5Ba7/DyT1pEROTWrKyssLq6Sim4pjMukskkKysrN/18hVQRkSPMrQ+H/fTK5bD698niJ7lSv8I//LJ/OKyklko4x1eC13WAqQM582E0uKfrZOJ0K+sAw3bfxUXodultbpLKOoAqqSIisj9s2+ZNb3rTQR/jwKndV0Qm2qfOX+UTf/DSQR/jdWs3OuGwn26lQjQIVr/64q/yU8/9FDDS7lsshYG2peFJu7SDu71OejglORYOThp8fsWwkqrhUyIiIvtHIVVEJtqLf77OZz+2cdDHeN1GK6mj7b6lVolap4bbdc1jlkW3VCKViYevk6HhKp/hlORBu6+9uAiAVyyG939VSRUREdk/CqkiMrF836dWdmlsH829l77vm92e6bgZ9uO6YfWv2CqGv1q2TXR2lm6pRGLKBkuVwOu5wSqf5JRNr1wByyI2NweYO6kA3UIRy7Jwsjatmj4/ERGR/aKQKiITq9308No9vHaPjts96OO8ZjutLv2+j5Ox6VZMi2p0Pofv+5RcM3Ch0DT7PQe7UiMRCydtK6ReJ2z3DT7L6MwMVsyMbYjlcqYSHU74TahdWkREZB8ppIrIxKqW3PD3R7Ga+kr3KGudGjs9834KrSCk5nJ0g0mBTiYe7lcVw617RKIWcSdGtzIcQAWYSvT8PN2i+SxT2bjafUVERPaRQqqITKxaeRhSm0cwpA6CZnLkHmVsfp5Sazi2PgypQSUVTLVQldTd3EaHZNrGsix65eEAqoHYQn7XrtRW7ej9fRERETkqFFJFZGLVSu3w90cypA7WpqRteiPtvkW3GD5nV7tvpYLf75tKqgYn7eLWvVfdN2svLNItDENqu+HR74/PonUREZHDRCFVRCZWreySSJl7h42toxdSh/co48N237nZsJKaiqV2VVLpdultbwchVZXUUe2Gt2uVz2i7L5jhSd2RSqrva/iUiIjIflFIFZGJVSu7zB2bIpGKHc1KamNYSe1WymbYj22HQ5PePv/2YUgd7PoslUhlbHZaXXrd/sEc/BByGx2cjE2/1cJvtV7e7ru4QG9zk36nE+5K1YRfERGR/aGQKiITq1p2yeYc0rOJIzk4ya17xBJRYvGouUc5sn4mE89wKntqV7svQLdYwtGu1JcZrPIZTEm+vt13sIamVyqFIdVVSBUREdkXCqkiMpF63T6NrR2yOYepmcSRraTuvkdpqn+lVokFZ4GF1AKVdgWv5w1DaqmEkx6EVIUsgF6vz06rSzJt0y0HA6iua/e1FxcB8IpFUtOqpIqIiOwnhVQRmUj1Sht8mM4lmZo5mpXU9siwn97IsJ+iWySfyrOYMsGq5JZ2h9SMeY1CqjG6ymd0ANXV2lX+y/P/Bd/3w0pqt1AMK9EKqSIiIvtDIVVEJlI1WD8zqKS69Q693tG6o+k2vDAwdSvDdt9Sq8RCaoHFKRNSC60CEcchkk4HITUevl5eYQBVbp7f/eLv8p/+8j9RaVeIBZXUbrFIPBkjlogqpIqIiOwThVQRmUi1UhBS8w7pmQT40KoerdDh1k27b39nh36jQWw+R9/vU3JL5J1hJXXXGppSCSerdt9Ro6t8wn2zc3PhAKqN5gbRmRmwbbpF81maXan6/ERERPaDQqqITKRa2SVmR0hl40zNJICjtYbG933chkcyE6c3co9ye2ebbr9r2n1HKqnmz3N0SyXiySiRmKWQGhhUlJMZ0+4bmZ7GisfDVT4bzQ0sy8LO5/EGa2gyCqkiIiL7RSFVRCZSrdwmm3ewLIv0rAmpR2l4krfTo+f1g+rf4B7lfBisFlILZOwMTsxho7kBDCuplmWZkKXpvsDonVTT7hve7W2ZQBp+fouLdAtBSJ1WSBUREdkvCqkiMpEG62cA0jNJ4GiF1OE9Snt4j3I+F7ao5p08lmWxmFoMw1Ysn6dbLuP7Pk4mrkpqYPA5JKdiwZRkE1LLrqlQhyF1YYHurkrq0fn7IiIicpQopIrIxPF9n1rZJZsz4TQxFSMaixypCb+DHadmt+ew3Xe0kgqwmFoctvsu5PFdl36ziZOxtecz4DY8ElMxItEIvXKZaG6eXr9HpW3C/3pzHYDY4gLdQnAndTrOTrNLr3u0hm2JiIgcBQqpIjJx2g0Pr90LK6mWZTE1E6e51T7gk908txFU/zKja1Pmw6ppzjE7UxenRkLqYA1N0exKddXuC5jAP9gdO9g3u9nepO+bALrRMpVUe2GBfqtFr9EkpeFTIiIi+0YhVUQmzmD9zOMDFxoAACAASURBVHQQUoEjtyt1VyW1XCGSzRKJxym5JWYTs8SjJkQtphYptUr0+r2X7Up16x183z+w93BYtBvXTUnOzVN0TdifTkzvupMKZg2NdqWKiIjsH4VUEZk4tZEdqV6hgHftGunZ5JG6kzqopDoZszZldNhPPpUPn7eQWqDnm9bV3SE1Ttfr4+303vjDHzJuwyOZtsMpyaMDqN6Rewdlt0y33yWWNy3U3WKB1LRCqoiIyH5RSBWRiVMrmbbeTC7J+qPvY+3f/BumZhI0t49OZbFd94jGItiJKL2RibSlVsmE1PoGFD4d7kottoovC6kwHMA0ydyGZ0J75eUDqL4q91Vm92yrRGxxEFKLYbuvQqqIiMjeU0gVkYlTK7ukpuPY8Sg7X/gC3uUrpGcS9Lp92s2jEdrcRgcnY2NZFt1KhWjO3EEtukUWnAU4/wH49e8c7kptFohks1jxeNjuC9Ca8DuVft+n3fDMKp/BlORgAJWFxVfOfyVg7qUOKqleoTAMqdXJ/vxERET2g0KqiEycWtllOu/Qb7fpbmzQLZdJZaLA0VlDM2hRhWDYz9ycmUjrVkwldeslqF5lMTELmJBlWVa4K3U4+OdohPL9suN28fs+ybQ9nJI8P0/JLTGXnGMlswLAemOdaHqKyNQU3WKJmB0l7sQmPuSLiIjsB4VUEZk4gx2p3tWr5gHfx/FbADS2jkhIrZsWVb/ToV+tEs3Ns7WzRc/vkXfyUFsDYHanhR2xhxN+c7ld7b6TPp12uG82PpySnMuFbdNLU0vAcMJvbHFxuIYmG1clVUREZB8opIrIROl5fRpbO2RzDp1BSAXiO9vA0amkDibSdjc3AXOPcrB+xoTUawBEatdYSC1QaA53pXZLJZygCjvpIXXw/gftvpF0mkgiYQZQOXmm7CkydmY44XdhgW7RfM6pbJxW7Wj8fRERETlKFFJFZKLUN9vgw3QuSefylfDxeK0IFkdmDc1gt+f19ygBFiJx6AXvo7rKYmoxDLCxfJ5uuUwsHsVORHFrk93u645UUkenJJfdcjgleXFqMQyp9uICXnFYSZ30dmkREZH9oJAqIhOlOrJ+pnPlMlYiAUCvWCCViR+JSmrX6+Ht9EhmbHoj9ygHuz3z3ZHgFITUsN03n6dfrdLf2cHJ2BN/p3JQSTUraMwAqm6/S6VdMRVpYGlqaXcltVTG7/dxsnFa1cP/90VEROSoUUgVkYlSKwUhNe/gXblK4i1vIZJK4W2sB2toDn/oGFTvRifSDu5RWljMt1vDJ9dWWZxapNAs4Pv+yBqaMk4mPvHtvoNpzk7aNgOo5ufZbG/S9/sspMw03+Wp5WHIX1gEz6O3vU0qG6fT7tHtaNesiIjIXlJIFZGJUiu7xOyICRhXrhA/eZLY0hLdjQJTM4kjMTipvatFdbDbc55iq8hccg67vm6emDkWVlI7/Q7bO9sjIbVoQuqE70l16x6xRJRYPGpCam4+3JGac8xan6WpJTbbm7S7bWILwa7U0TU02pUqIiKypxRSRWSi1MptsnkHPA9vbQ371EnspSW8wgbpI1NJHQ776VXKRFIpIo5DyS2Z6l/tGkRicOwOqK6GFcFCqzASUkukMvbEV1LdYABVOCV5fuRub/C5DSb8FloF7MUgpBaLCqkiIiL7RCFVRCZKuH7m2jXo94mfPGUqqesbTM0m2Gl1D3375q5hP8E9SiBcm0Ltmqmizp4KKqkmWBVbxV0h1cnEadc9/L5/MG/kEGjXvZdNSX5ZJTUVrKFpboSVVE+VVBERkX2jkCoiE8P3fWoll2wuGa6fiZ88gb20SLdUYioTAw7/hN/RYT+De5QAJbc03JGaPQbTK9BpsBh1ABOyonNzEImEIbXf99lxuwf2Xg6a2/BI3mBKsoXFvGM+13BXanODWPADgW6xRCprhm4ppIqIiOwthVQRmRjthoe30zOTfYP1M4M7qfg+SasNQPOQ30t1Gx6RiEUiFaNXKRPLzZuJtG4laPcNQmr2OAC5HZeIFaHQKmBFo8Tm501IzWpXqtvo4IxMSY6O3u3t96HyBRanFgETUq14nOj8PN1CIfz8FFJFRET2lkKqiEyMwfqZ6cH6mVSKaC6HvWQqZYn2NnD4K6nteodk2sayLNPuOz9Pxa3g4weV1GswfRymTwAQq6+Tc3IUmsM1NN1SCSdt2lUnOaSG7b5hJTU33JH6iafhP38tiW6HueQcG62RNTTFItFohOSUjauQKiIisqcUUkVkYtRGdqR6V64SP3kSy7KILQYhtWnuIh724Uluw8PJ2PjdLr3t7V33KBeiDnTbpoo6vWJecN2u1Gg+F66gAWjVJnPCr7fTo+v1bzglOe/kofI581luX2Fpaon1ppmabC8s4JXMTtrUdFyVVBERkT2mkCoiE6NWMu282VzSrJ85YSqN9pJp56RSwE5GD39IrQf3KDc3wfeJ5UywAsj3gqFP2WMwlYdoPAypg+fE8nm65RJOZrLbfUfv9vYqZaxUikgqtXtKMpiQmloaVqIXFugWzGfpZOK0aof774uIiMhRo5AqIhOjVnaZmo4TjYJ39SrxUycBiGSzWKkU3Y110jOJQ9/uO7xHaap/u9amdIKzZ49DJGJ+ra6yODWspMbyeXqVTZJONPh+k1lJbTeDKclBu29s3tzt3Wxvmsm+YUi9ytLUEhvNoN13cZFepYLveaSyqqSKiIjsNYVUEZkYtbJLNu/QLRTwPQ/7pAmplmVhLy7ibRSYOgK7UtsND2fXRNocRbdI1Ioy61bNk4KhSUyvhJXUptek0WmYNTT9Pv3tTXOncmIrqSOrfIIpyZvtTfp+/7pK6mWWppZoeA3z+S2M7JoN2n19f3LX+IiIiOw1hVQRmRjVktmR2rkynOw7EFtapLuxQfqQh9Rer89Oq4uTsekGE2ljQSV1PjlPtL4OVhTSZp8n0yfCkApQaBWu25U6wSG1sbvdN5qbH+5ITcxCw1SeqV7dtYbGXjSfZbdYJJWJ0+308XYO925dERGRo0QhVUQmQs/r09jeedn6mQF7aRlvY8NUUqsd+v3DWRlrN4YtqsN2X1NJzaeCyb6ZZYiYVl6mV6B+jUXH7PcsNAvYu0JqPKwoTpqwkhq2++aGbdN+BAj+DmxfYXlqGYCN1gaxBfMDAK9QJDU9GD41mUFfRERkPyikishEqG+2wYfpXNKsn4nHzX7UQGxpkW6pxFTWxu/7h3atyCBYJYN2XyuZJDKVotQqBSF1zayfGZg+Dn6fhb758uWV1PjEVlLbDY9I1MK2CaYkjwyg6gbBffpEeCcVYL25HobUQSUVFFJFRET2kkKqiEyE6nXrZ+yVFazI8P8C7cUl6PdJRkyr72EdnjRoUXUyNr1Nc4/SsixKrRILzgJU18xk34FgDc3CjplsXGgViF7X7tua0JDqNsy+2d7WFvg+0dw8ZbeMhcV8u26edOJroFUmF3WIWBE2mhtEZ2fBtukWC2El9bD+UENEROQoUkgVkYlQKwUhNW/upA5afX/quZ/iRy/+KPZysCu1sw0c3l2p7bBF1VRSo7l5Or0OWztbw3bf7Ggl1azZSTSLzCXnKLQKROJxotPTYSV1p9ml1+sfxNs5UG7dDKDqhTtScxRb5nOK1YP7qCfvMn9W3yDv5NlobmBFIsTyOVNJzSYAVVJFRET2kkKqiEyEWtklZkdwMjadkfUzF9Yu8LH1j4Wtv4mWCSyNrcMZUkcrqWYibY6yawYoLcSmoOvurqQOAmv1KoupxZFdn3kznTbYldqewDU07WCVz3BKsqmkhmHfnoKl282Tt6+wNDXclWovLOIViyTTNpalkCoiIrKXFFJFZCLUym2yeYd+pYLfamGfPInX91itr1JoFogELbDRrQ0iEevQVlLdugcWJKbMdN9d9yj7lnlS9jjP/c0mv/kXVyCRBmc2nPA72JUazeXCSmr4fSeM2/BIpndPSS62iuSdPNSvmbA/EwzXCkLqRivYlbqwQLdQJBKxSGa0K1VERGQvKaSKyESoll++fuZa4xpdv0vX71KJ72A5Dr2NDVIz8cMbUhseySkbC5/e5lZ4jxJgYTDsJ3ucX7rwN/z733vRfB3sSl1ILYSBNpbP0yuVR0Lq5IWswb7ZcEpyLkfJLQ13pGaPQXoJInY44XejuYHv+8QWF+kWzWeZyiqkioiI7CWFVBEZe77vUyu5ZHPJXetnLtcuh8/ZaJn9l16hQHomcWgHJ7XrHbN+Znsber3wHiVAfqdpnpQ9xup2i3q7S9X1hrtSpxbZ3tmm3W0Ty5t232TatPtOWkjdtW+2XMFKJOg7CTbbm+Sc3PBubyRiQn5QSd3p7bC1s0VsIU+/0aDfbJqQWj2cf19ERESOIoVUERl77YaHt9MzldSrVyAaxT52jJeqL4XPuda4Rmxpie5gV+ohDaluw8PJxOmWgxbV3Dwlt0QsEmOmuQVWFDJLrG2ZQVFXN1tBJdXcSQUotorE8nl8zyPpm+dNWrvv6L7ZQdv01s4Wfb/PgpOD+jpkzW5UZk5C9SpLKXNveaNpfqAB4BWLJqROWMgXERHZTwqpIjL2ButnpnMO3uUr2MeOYdk2l2uXScVSgNl/aS8t4W1skJ5J0tjewff9gzz2DbmDSmo4kXZ4jzJSuwaZJVpdn62WCWGrW66pCLarLNoZYPeu1Ehjk0jEmriQNQipyXScXrliWn1bJQDyVhz63eEAqpkTYSUVTEgNd6UWimG772H8+yIiInIUKaSKyNirjexIHV0/c7l2mdtmb2M2MRtUUhfNxNtsjO5Oj067d5DHvqFw2E8wkTY6b8KVmUhrdqQOqqgAq1utcFfqYs+EqI3mRhhSe+UyyYw9ce2+g/drKqlm32zJDUJqL/j3PpiMPHMKGgUW4zPAdSG1ZEJqv+uz0+q+sW9CRERkTCmkisjYq5XaAOZO6pUr4fqZl2ovcTp7muX0Mtea17CXlqDXw4mZKltjq31gZ76Rft+n3QzafcOJtHNm2I8zHPazuiukuuGu1MWOeT+Ddl8gnPA7ae2+7qCSOljlkxuZkuwFrd6DSmrw+c3tNLEjNhutDWILpt23WyiQyk7u8CkREZH9oJAqImOvVnaZmo5jter0azXsEydpeS0KrQInMyc5NnWM9cZ6uCs12akCHLp7qTtND3xIBu2+lm0TyWaHa1Nq1yC7wuq2CalzU/FdldRUo0jGzgTtvoNKoNmVOmkBaxDKk6kovc1NovNmSrKFxbxbN08KK6nmhxqR2qpZQ9PcIJqeIpJKhXdSAVrVyfoMRURE9strCqmWZcX26yAiIvulVnbJ5kfWz5w6ydX6VQBOTZ9iOb3MenOdWDAMJ+5uAocvpA6C1WAibXR+np3eDrVOjbydBq8ZtvvaUYs7TsxwddOFzJIZqBRM+C00C0TTU1ip1EgldbICVrth3q/daUK/H05JnkvOEauvQzQOqXnz5BlTSQ13pTaDXamLi3SLJZxBSNUaGhERkT1xUyHVsqyYZVnvA/7hPp9HRGTPVUvBjtSR9TMv1V4C4HT2NMemjuF2XVqzjvnzmgkhhy6kNgb3KOPhRNrwHiVR86TsMda2XY7NOJycT7G61cK3IqYqWF1lMbVIoVUAIJbPTXS7b2IqRn/L/EBiMCV5145UyzJPzhwzIX/bTPgNQ+rCAt1CgalsAlBIFRER2Ss3W0n9r8Anfd//sGVZy5Zl/WfLsv69ZVnfDmBZ1tdblvUzlmU9YVnW24LH/oVlWf/WsqyftywrY1mWbVnWf7Is6/+wLOsD+/WGRERG9bw+je2d4foZy8I+cSLckXoyc5LltFk1shFrYCWT9AsbJKdsGluHLKSOVFLNRNr5cCLtQj+YLDu9wupWi+MzDiuzKZqdnpn0O70yrKSGITVvKoEZG2+nh9c5fIOi9otb93DScXrB3d7ovPkszY7U9WGrL0A0Zr4OKqnFVpFev0dscYFusUgiFTMTkhVSRURE9sSXDKmWZf09YNn3/d8LHvqPwHPAB4APWpaVB34NeAL4MPALlmW9Hfgh3/d/EqgA7wW+B5j1ff9x4BuC7ysisq/qm23wYTqXxLt8hdjiIpFEgsu1yyykFkjZKY5NmQE568H+S69wOHeltndVUiumRdUNhv3sBMOSgnbf4zMOJ4LKsLmXehyqV1lILVBxK3h9j1guT7dcxslM3uCfdqNj2qYHq3xyuZFK6tpwaNLAYFfq1BI9v0fZLWMvmJCKBY52pYqIiOyZm6mkfgtw0bKs/82yrD8F/ldgw/f9HaALPAicADaCf74WuBsoBq/fAL4Z+PvB70cf28WyrH9mWdYly7IulUql1/+uREQC1VdYPzOY7AtwLG0CiVlDs0R3o0B6NkHjkIXUwUTaxFSM7uamafcdVFLdOlgRdpI5ivUdVmZTrMyaHbBXN11TSa1dY9HJ4+NTbpVNJbVUIhWG1Mlp+XUbppI6WOXD7DSb7c2gkmrafQu1Nr91ydxdZubk7l2pwYRf3/PobW+bXakanCQiIrInbiakTgEN3/c/hAmeo6/xg39Gv7Zu8Bwr+Of6x3bxff+Dvu/f6fv+nflgPYKIyK2olYKQmnfoXL0arp+5XLvMqewp6PfJxtKkYinWm+vYS0t4G4ezkurWPeJODKtZB88z9yhbJeKRONlGGdKLXKublt3jsw4rc6OV1BXoeyxa5v6kmfCbp99skrB7wfefnJA12Dfbq5SxbJvteJe+32chmoLeDmSO8X8/d4V/c+6vKNV3zPCk+jpLCTNMadeu1GANTat2uP6+iIiIHFU3E1L/J7Ac/D6GqZ6uWJYVB+LAbwBrwAqwhGkF/iNgcKFnCTgfPLZy3WMiIvuqVnaJ2RESEY9euYx94iTb7W2qO1UTUn/nDNa5hzmWPjaspBaLTGVt3LpHz+sf9FsIuY0OTnrYohoN2n3zqTxW/Rpkj7MW7Eg9PuOQTdpMO/buXal98342WhvhrlS7Y1auTEpI9fs+7YZnPstgSnLZNXdT837w89OgbRrgymbTVFL9PkuDz280pAZraFzdSRUREdkTNxNSnwKSlmV9L6aSehvwt4BHge/3fb8IPAS8B/g24N2+738a+BnLsh4FcsCPAb8AbFuW9QPABd/3f3+v34yIyPVq5TbZvEN31bRtXj/Zl8sXYe0TLE8tB5XURej1cGKm9bVZPTzVMbfuhetngLCSOnqPcm27BcBKcB91Zdbh6siu1MXg7mqhWRiG1NZW+P0nwY7bxe/7OJnB3d6RKcndrnlS9jjXqoOQ2gpDfqZRIRVLsdHcwF40IdUbVFLrHn7ff/n/oIiIiLwmX3Lvqe/7DeC7gy9/Mfj1+657zkeBj1732H+8wbd7z+s4o4jI6/ay9TOnTnK59tcAnHIWoXoFsDiWWuSvyn9FbMncOUz2THWxGUwGPgzajQ6ZeYdeZRXAhKuNEm+ZeQtU/wi+7G7WtlwiFixNJwE4MZvi86UGTL8VgGyjTDKapNgqEsu/EwBru0ws7kxMJXXwPpNp26zyyecptgYDqEzIN5XUzwBwudKCoE3cqq2Gu1IHIb9bLOK8LW4qtE0vHEQlIiIir8/NrqARETlyfN+nVnbJ5pJm/QxgnzjJ5dplolaU44NAgs9y1KG6U6WXmwEg7prq4mEannR9JTWay5lKanwavCZMH2d1y2Upm8SOmv97X5l1zK7URBYSWazaWriGJrYQhKwJ25XaDgZQOWmbXmWT2LyZ7GthMd/aBitKP5XnWrUNBJXU7HHACocnbTQ3sOJxonNzdIslUlkTTLWGRkRE5NYppIrI2Go3PLydHtmcg3flCtH5eaLpKV6qvcRKZgV784vhc48FV09LafObeN3sEj0sw5N8f3CP0rSoEo2yk7JpeA3ylm2elD3G6rbL8dlh5ffEXIq216fc6Ax3paYWKTQLRGdmwLZHQupkBKzBlORk2t41JXkuOUesvgGZZSpuj07X/F24utmCWNyspdk2a2g2WmZYfWxhgW6hwNS0QqqIiMheUUgVkbE1WD8zHbT7DtbPhJN9y38dPne5Y8LoeqyBlUhgldaJ2ZFDU0ndaXXp9/1gt2eZ2Nwc5R1TUR0O+zGDkwarZ2B4N/XqVlANrF41IbVVwLIsYrmcqQRm7InZ8zkI4wm/PZyS7F5/t9f83Zmbipt2XzD3UoNKatkt0+l1iC2aXamDFl+FVBERkVunkCoiY6s2uiP16lXiJ0/S9/tcqV0ZhtSZUxBNcKxVBWC9uUFsaZFe8XCtodnVolquEM3lwnuUC575s256mY1am+Mzw0rqILCaCb9BJXVqkVKrRN/vh7tSJ6ndd1BJjbXNv/PovGmbzjk5qK9D9hjXgpB615vnKNZ3cDs9M+G3eoWllLm3XGgVsBcW8IpFUtNmtY9CqoiIyK1TSBWRsVUrmTuF6TR019exT56g2CrS7rXNZN/SX0P+K2DmJLnqBnbE5lrzGvbiEt5GwYTUrcMRUsNhP6MTaVvBRNqdJmCx0Z+h1/d3tfsOKqnhrtRWhYXELF2/y2Z701RSy2WcjI1b7+D74z+dtt3wiCWiUDX3jmPzc6aS6uShumYm+wYh9Z1vNntRV7daZldqdY0lx9zlNWtoFulVKtjRPlE7opAqIiKyBxRSRWRs1couU9Nx/OI6APGTp8L1M6fSJ6Dyech/OcyeIlI1bZzrjXXs5SW8jXWmZhKHpt3XHamkdivlXWtTFlpbkF5krWbWp4xWUqcSMeam4lzdHNmV6keB4RqaQSW13/PpuN038m0diMG+2V7F7EZlboaKWyGfCAZQZZdZ3XJJJ2J85fFpIJjwO3MS/B5LwecX7kr1fXqVCqlMnFbtcPx9EREROcoUUkVkbFVLLtm8Q+dKsCP11EkuVy8DcIoo9HYg9+Uwexq2LnNs6hjXmteILS7RLZZIz8RpVncORXVxdG2Kafedp9gq4sQcpmqFXfcoV2Z3r8w5EUz4DXel9kwQ3WiZNSq9rS2STiT43xn/lt923TNhP5iSXE9H8fHJY8LnoN332EySU3OmXfrKZhBSgaWOqdDvmpBcLJKajuOqkioiInLLFFJFZGyZ9TMOnSsmmNonTvBS7SWcmMNCwwQUcl9u7qW2t1lOzrPeWCe2tAjdLo7dpd/1w/ugB2lQSU1YHfxOx6xNaZXIO3ms+jpMm6FJAMdmdofUldnU8E4qsNg2zxtUUgESvnlsEib8ug2zy3QwJblim+pnvhf8MCJ7nGtVl+MzDnNTcabiURNSp01IdRoFphPTbDQ3sBcXAfAKBZxMXO2+IiIie0AhVUTGUs/r09jeCdfPRLJZojMzXK5d5mTmJJHK580TB5VU4Fg0ScktEQmqY8l+E4DGIbiX2q4P7lFuAhDLzVN0i+RTw3uUq1suuXSCpB3d9dqVOYe1LZd+ehmwmGtuEovETCUwCKl2pw5MRiXVbXTM+plKmejcLKXBlGQv+PecPcbalsuxGQfLsjgxlwpCqgn5bF9heWqZ9ea6afcFMyF5WiFVRERkLyikishYqm+2wYfpXDJcP2NZ1nD9TOmzkMpBag5mTwGw3Lfw8dmejgEQb5tAeBgm/A7vUZpAFQ0GJy0kZqFTD9t9j1/X6gumktrp9Sm2+pBZIlJbY8FZoNgqDkNqywwRmoQ1NIN23165Qmx+OCU53zYDqFqJHFstL6xIn5oPQqqdhPSiWUOTWmKjuUF0bg5iMdPum43jNjz6vf4BvjsREZGjTyFVRMZS9QbrZ7yex1pjLVg/8znIv9U8ecaE1GM75q5hMW3ubCbqZjDRYRie5DZ236OMBoOT8pGkeUL2OGvb7svuo8INJvxWr7I4tbjrTmW0Zt7ruLf7ejs9ul4/bPcdDKCysJhvViC9wLW6CZmDz+3kXIqrmy36fd/cS92+wuLUIhvNDaxIxAyfKhRIZeLgD1uzRURE5PVRSBWRsVQrmZCambHx1tawT55gtbFKz+9xevq02ZGae4t5sjMDyRmWXbM3cy1ax4rHiW5dw7IOSSW13jHBatOE1J3pJG7XZSEY9tPPmErqyszLQ+qJG+1KTS2aO6nz82BZ+OUSiVRs7Nt9RwdQdStlYjlTkZ5LzhGrb+waQDWopJ6cS7HT7VOs75gJydtmEnStU6PltbAXFuiWzOAk0K5UERGRW6WQKiJjqVZ2idkR7EYJej3iJ09xuRZM9rVnwN0091EHZk+xVC1gYbHe2iC2tER/YwMnGz8UldR2Y9iiSiRCJW7CZL7XA2AzmqPT7b9Cu6957OrgXmVtjcXUAoVWAaJRonNz4Rqaca+kjq7y6ZUrROdzZkdqagFq13btSB2s8jk5PwWMTPitrrLkmIFJGy2zhsYrFEllE4BCqoiIyK1SSBWRsTRYP+NdXQWC9TNBSD3dCUJn7q3DF8ycwt6+Qj6V51rjGvbiIl6hQHomceCVVN/3ceseyaBFNTo7S6kTDPvptAGLq12zz/P4DSqpSTtKPpMIKqknoNtm0c6w09uhulMd2ZVqj31IHUxqjke6wZRkU0nNp/JBSDXrZ6IRi4WMCZ0nd62hOQF9j6WIqZpuNDeILS4Gd1JtAFrV8f4MRURE9ptCqoiMpVq5fcP1MzOJGaara+ZJg3ZfMBN+t69wbOqYmdq6vER3Y4OpQxBSvZ0evW7f3EmtlMNgBbDg1iC9wGrNVFRXgtbe663MOlwd3ZXqWwDhhN9uuWxWqIx7u2/DBEjbM9OMY7ngbm9iBtrb4WTfpWySWNT8J/L4jEPEgiuVZnh/edkLds02TSW1X6+TjJl/B+Me9EVERPabQqqIjB3f94MdqUm8K1ewHIdYPj8y2fevIebA9Ak+X6zzmY2amfDb22E5MRNUUpfwikWmZuIHHlIH90SdTDCRNghWAPl6BbLHTJUUbtjuC+Ze6uiu1IUgZBVaBWK5XNju226Md8AafJbxtrl/zNwsFbcyHEAV3O0drUjHYxGWp51gDc0J/tEm3AAAIABJREFUABbadSysIKSa4VNWtYKdiKqSKiIicosUUkVk7LQbHt5Oz1RSR9fPVIOQWv5ryN0GkQiPfvh/8n/+178a7kqNJNlobRBdXADPI2X32Gl18XZ6B/Z+BtU/Jx20+87nKLVKpO00qfpGMNm3xbRjk07Ebvg9VmYdrm279DImpC7tmB2wYSW1UsFJx8wKlb7/xryxA9BudIhELaxg32wzE8PHJx9UlgeDk47NJHe97uRgV+qMCal2bY2ck2OjuYG9aO6nesUiTjZOq3bwd5hFRESOMoVUERk7g/Uz0+H6mRO0vBZFt8jp7GkofxZyX47v+7y4UeOLpQb+YA1NH7r9Lq3gHmKSFnCwE37bQfUvmbHDtSnFVnHkHuVx1rZuvH5mYGU2Rbfvs+E5EHOYb24RsSJmwm8+D90uiWgX/OG9zXHkNjySaZveZhmALcesm8l3zQ8hepljbFTb4WTfgTCkxqcgNR9O+B20+wJ0C0WmsvGJ2DUr/z97bx4mWV6X+X7OGntEZuyZlVvTXdXsoIOiLCOKLZsOoo5enWe40NggiHgRvCPOyGUU5mFx414aoWkB1ytexHEB2VQQUUbAlqYbuqu6ujIjKzNjzYz9RMTZ7h+/czKru2vLqswqIvv3eZ5+HqoyzjkRJ5OseOP9ft9XIpFIJIeJFKkSieTI0Q1EaiprYlcqGEt7oUnL8TK01yF/gmp3RG/kMJi4NLQioDA3EV2p2ynhrJnjNnB9u1JDJzWqu/jDIVouKxJpI1kYd3bdv/OFJoUsZsOuVDHya3Q3yEfzu04qQMQVgvwo71RaPZtY0sRttUBRqJvi+12ciJ+ZhpLF8fxHjE0v5eI0+xMGYyfoSl0XInUogpOAIDzJlOO+EolEIpFcJVKkSiSSI0e3IYRH3O3i2/ZD62d8FfAhf4KTtf7uMWsdF9LzzA+6AGwlhNCI9IXjNtgZXcNX8FDCPUp9HIT95PLCSdVFNYof7KReaB8V9gKV1s/tSk2UqA/ruzuVxqQbXO/oiqxRf0IsZeA0w5RkMfabH3YhlmWjL0adz+ekAiJ8amYJ2hVK8RLVQRUlHkeJx3HqtWDc9+jeP4lEIpFIrgVSpEokkiNHp2mRyJh4WyLF11xeYrW7CsDSUAgx8ic4VevtHnOmKZJb57pVAM7qXRTDwOxsAdfbSbXRdBWlK2pntFxW1KYoovKkbxYZTtyLOqnzM1EUBc6GCb+dDYrx4t64L2AMdsT1jnDCr3BS98amG1YDBYVcvxXs9ooPIx5+L5dzQqSutYLwpM46c4k5LMeiZ/cwikXswEkdDx1c27vmr00ikUgkkqOCFKkSieTI0Q06UsP6GXNxkbXuGuVEmdj2KqBA7ibur/bIJkx0VWGtNYDZZeLtdWYiM2wNqujlMn5tEzOmM2hfP3ds1BPun7ctXL9RJsrEm1AIAo42/Rxw4foZgIiuUUpFWd8OulL7VUqxh4776j2RGHyUdypHA5to0sRtNtHyosonG82i9/Y6UuEiTur2UNTQOCPKmnhMuJfq1IRIhaN9DyUSiUQiOWykSJVIJEcOUT8TE/UzhoFeLu/VzzRPiroZI8rJep/HllMszMZYbQ5Fwm93k7lEmc3BJkaphF27/l2pYdiP0xRO6k5MiNOCLRzP1UkG4KLBSSD2Us+e25WqRunbfYaqg5pKoW7XUJSjO+7ruh7joSPGfVst9Fxe7PbGi0EAlehIPV9KciZmkIrqD0n4Lbvi+1AdiL3UcCcVju49lEgkEonkWiBFqkQiOVK4tke/PRb1M5V1jMVFUFVWu6tBsu/J3WTfB2o9TpRSrOQTu+O+4DNvZNjqb6GXyzjVGskZk/513UmdEEuZOC2xH9uMCgFUHPchUWC9KzpPLzbuC8JpPbcrteSLfwLqwzp6oYDbbBBNmVhHNN03TC1+yLjvsEEhmoNhE9LH2Gxbj3BRARRFYTkXF+O+M0sAlMciaCrsSnWCChpAhidJJBKJRHIVSJEqkUiOFL3tEfiQyUeZVCqYi4vsjHfoTXospxah9QDkT7DRthhMXCFScwnWWgP8QHzMqRE2B5vo5TJ2rfYt4aTGkgZuq4WWyVC3xdhvYdAO9igt4qbGTNy46HkWZmNsdSzs5DwApcCJ3e1KbTaJpwysIxr8E+7aRgwP37LEuK/VoKAHY9KXSEleysbFuG9GOKm5wTa6olMdVjGKRfzJhIgvPsyQ4UkSiUQikVw5UqRKJJIjRdiRmgpEqrF8Tv2MlgBnFCT7itCkE6UkK7k4g4lLyxTibd4Dy7Fw8mmwbeIRj2F3gudenzAcIVJNkUibz9MYit3RQq/+kI5URVEuep7F2TieDzWyAJRGAyAQqfk8TqNBLGUe2eCkUVDlY7riZ0TNztKyWrsBVKTnApEaPe/xi1nhRLtmCqIzaF0RPhWO+wIYlqgskiJVIpFIJJIrR4pUiURypOg2hABJqBb+cIi5tMxqZxWAlXEgHM6pnzkejPsCnBmnQIswH3SldjJiLzGKhe/DsHvtxZszcXHGLtHU3ohqfVgnbaaJhnuUl+hIDQl3VitdHxIFigOx4xom/DqNBrGkcWT3KcMx5rDKZ5Ay8fEpBJ899CMleiPnvOO+AMvZBBPXo9odib3UdoVyoszWYAu9WBQP2q4TietSpEokEolEchVIkSqRSI4UnaaFbqjoO6I6xgycVF3RmQ86TynczMlaj1I6QiZmsJILRGrLgpkl5oYdAJop8fDIRPy53772e6nWOXuUbrOJHoyoFmN5GHXgMjpSQxaDhNpwLzXS3WI2Mrs77uuPRkSjytEVqWHfrCWqdroJ4TwXHPF6Nz3hMF/oXoYJv5VWkPDbXqecKAc7qcJJdep1YimTYff6jYdLJBKJRDLtSJEqkUiOFGH9jF1ZB/bqZxZSC+itUxDPQTzLqVqfEyWhQhdmY+iqwmpT1NDMd2oAbMSFKDWHwnG8HnupoWAUwUkttJwY9y0Y4rmP4nN0LJtjMxeunwkpZ6KoCqyHCb/dDUqJkhCpRVFDYypjJiMXx3YP70VdJ6z+BBTQ++L72YyJwKniaACRNGeH4p/ECzmpD6mhyQROarxMbVhDLYgaILtWI542pZMqkUgkEslVIEWqRCI5UnSboyDZdw00DWN+/pxk31OQP4Hn+Zyq93ZFqq6pLMzGguTWZTI7FWJ6jIraAcPA7FSB6yRSAyc1EgGv3xeJtFaDghoBoK6EHamXdlINTWUuEwuc1EXonKUUL+6m+wJEXJFYexT3Ukc9m2jcwGsFY84R8SFEftgOxqbFny80Oj0/E0VTFda2ByLh1x5QNtM4nkPb66PNzooamowUqRKJRCKRXA1SpEokkiOD7/tBR2oUu7KOMT+Pb+hUupW9jtT8CdZ3hoxsjxOl5O6xuzU0sysoozbz8RJbVhWjVEKtn0XVFPo7116kjgIn1bTFDq2ay4pxX18DYMOdBS48ovpwFmZjgRO4AJM+RXNmdycVwAj2NY/iyO9u32yriZbJ0LC3UVDI9eqQnmezbWFoCoVk5LzH65rKsZkYlW1rrys12GcVI79FnFqdeEqK1OuF7/vX+ylIJBKJ5ACQIlUikRwZRn0be+wGTqqon6kOqky8CcvRPAxbjwhNCnlEDY2ZYbO/iV4u4daqJDLXp4YmdFKNkdiLHWeiOJ5DwRXjuKfHaQAWLiM4Cc7TlapG2Bnv4GbFeYyhqLc5kk5qf0IsZeCek5Kci+XQu1vCSd2xmMvEUNULpyQvZeNUts/pSg1qfETCb3HXSbVHLvbk6I1Mfytz5u4md/7CF2ht9q/3U5FIJBLJVSJFqkQiOTKE9TOZQKQay0usdlcBWAn1QhCaBHC8eI6TGtTQ7ESCGholwtZgC6MkulKTs9dJpPZsVFVB7Qnx2IkLAVWcDCGeZ73rYeoq+Qu4fw9nMRuj1hsxScwBUPLE+ZqqhRKJoPdEuJTVP3pO4G6VT5CS3LAaFKJ56NcgfYzNy0hJXsrFqbQGuyJ1biQEUZjwa9fFTipwZPtmvxVpnu3x6d+9l4nl0FjrXe+nI5FIJJKrRIpUiURyZOgGIjURsfE6Hcyl5b2O1KATlPxxTtZ6HJuJkYoau8eGNTSrrqgSmfOgPW5DMY9TrRLPmPSvi5M6IZo0cLfFHmUrCPspWD2R7BsIq4u5f+eyMBvH96GKGO8tBcm2NUuM/GpBKrJ1Hep2DhurNwmqfIKU5GGDgpkG39ut8rlQaFLIUjbOztCmSwLMFJlenagWpTqoYhSLuM0WsYSoLpIjv9eGQWfMx2+/m0hMB2Xv94BEIpFIphcpUiUSyZGh2xDBN9FhAwBzSST7xvQYhfYG6FHILHKy1uf4OfuowG4NzQM9DaIZ5ifije5gNopv28RjPoOd8TXfebN6thhRDcJ+6lEhlIv9FqSPsbFzeR2pIYvB7uraOA6aSckSTuBueFKrhmaoR24n1fd8RgOHWCIY983lqQ/rFNQoAE6iTK074thM9KLn2U343RF1RUrnYTU0vk/EE+FTUqQePs7E5RO/83VGA5sXvebJJGcidFvXvipKIpFIJAeLFKkSieTI0GlaJDIm3kZQP7O0tJvsq7ROQe44LiqnG3v1MyEPqaGZWWZ+IHZAd9Li12RMGeHYHuOhc01f06g/IZo0cZot1FSKmiM6PvOdLcgcEx2p+xCpC7siawzpY5QG4ny1YQ09n8dtNIiljCMnUseWg+/5RKIK3mCAmp1le7RNIfhnsKXm8fxLB1A9tCtV1NCUEiWqQxGcBGAEPaxSpB4uvufzt7/3TeprXW659QkUllKkclHppEokEskRQIpUiURyZAg7UicVMeJrLC6y1lk7J9n3OGutARPHe4RI1TWVxWxc1NDMrjDXFV2p9aRYZo04Ys/tWu+lhk6q02qKPcphg9nIDIa1g52Yo9kfX1b9TEg5HUVXFc4GXamJ7hZJI7mb8Os0GsSSJsMjFpwUiu6IL75/o3QEH5+CK+J514OU5EuO++YCkRqGJ7XXKcfLu8FJAEbQwypF6uHyLx8/wwNfrfPdL7mRxzxVjK+n8zF60kmVSCSSqUeKVIlEcmQQ9TMx7Mo6ermMY6hsDjZZTh6DnbUg2VeIzRMPG/cFWM7FgxqaZQrbFXRVZz0mXJnIUAiPay5Sg7AfkUibo27VKZgzAOzoeeDy62cANFVhfiYmxlV3u1JLwkktFvC6XWIJ7cg5qWFKsumK8eZeUlT4FCYj0GOsWyLs6FIiNR01mIkbrG0Pxf0bdyhHZmhaTchnAXAbNaJJQ4rUQ+T+/1XlKx9f5XHPmOPbblna/ft0Lkq/Pca1vev47CQSiURytUiRKpFIjgSu7dFvjx9SP3O2dxbP91hWIoAPhb36mZuKjxSpezU0K6jumHKswKq2A4aB2a0DXNPwJNfxmFhO4KS20HOiNqWgCzdvy88B7GvcF8Roc+ik0tukFC8+pCs1YnhHTqSOApGqBz2w23EhYopBANVmR3xfL+deLmfjoms2TPhVTDzfYzvmgqbh1BvE0ybDzrUP2no0UH2ww9//wX3MH5/he37qZhRlLzQsnY+BD71t6aZKJBLJNCNFqkQiORL0tkfgQyYffWT9jB0IrsBJXczGiJv6I84R1tB0okENjZFmayhSW7Wm2HO9lk5qKKxiSWOvNmXYoKiI515xxIjqfpxUgMVzu1J9j5KR2gtOAiKMsXr2NQ+JOkxC0a0PRJVPMybubWGwLVKSdyxyCZOooV3yXIu7XamLAJQdMRJes8Q9dGqihuaoCf1vBbpNi0/8zt0kZyO84FVPQtMf+jYmnRfBV3LkVyKRSKYbKVIlEsmRIOxITSYV3GYTc2mZSrcCwFK/DSiQu4mTtR43P2wfNSSsoal4QqzNqRE2B5vo5TJ+bYtYyrimTmrYVRqNqnidDmouS3PUpOAJ8XjaSqGpCuX0xRNpH87CbIxGb7zblVpUDBpWA3JC9JreENfxsMfuxU4zVYTjvkbQA1s1LRQUst29jtRLjfqGLGXjbOxYOKlApI7Fz164l+rU68JJleO+B8rEcvj4e+/Gc31e9LNPJpo0HvGYVE58D7stGZ4kkUgk04wUqRKJ5EjQbYg3pbGx2B01lxZZ7a6SjWbJ7KzBzBK2GuFMc8DxC4jUGwKRenI8CyjMuz6NYQOtWMCu1UjMRK6pk2oF4UWGL17bOB3F8z2Ktg2xLGs9XwQhafv7Vb4YJNRuhV2pHvj4dFPCRTSDkdij5ASOejZGRMPfbu6mJOdiOfTephj3bV9+SvJyLo7j+WzZCdBjlAdtAKqB627Xa8TSJsPO5Ei50dcTz/X41J33slMd8rxXPpHZcuK8j0vMRFA1hW5TOqkSiUQyzUiRKpFIjgSdpoVuqGiNDUDUz6x1g2TfxknIn2C1OcB2/fOGJoHYR9RVhQd3HEjNMTce4eMzzqdwqlUSmQj9nWvvpBq22KPtpsSv7MJoENTPDPc96gvspgGvOSKAqWSL11Q3R6BpGJYYibWOUMKvNZics9ubo2E1RACV5+Cn59nYh5MaivxK0JWa6G2SMlLCSS0Ud3dSHftoudHXky/+2QNU7m3xPT95gsXHZi/4OFVVSGaj0kmVSCSSKUeKVIlEciQI62ecs2LE1whFamoJWqegcPNuaNLx4vmd1IfW0CwzPxQOWS9j4k8mxOPXdic1FImmJZ5HOyZcueJgG9LH2NixWNhnaBLAwqwQWWt9FWKzlEYDAGqjBnouh94VI7FHaVx11LOJJgzcZhMtL3Z7C4Zw4waREsOJy/zM5Y1Nh12pa62whiboSh1U0UslkZAcFWE+w87RuYfXi3s+f5a7/+4sT3nuIk949rFLPj6di0onVSKRSKYcKVIlEsmRoNsciWTftQpaNssootCwGiybM+CMIH+c+2s9VOX8yb4hezU0K8x3RFdqKy2+FlPHjPo2jn1t3LFR3wYF1J5wNuth2E+3jpuao9od7asjNaSYimBqKme3RcJvuR+cPwhP0tpV4GiN+1p9m1jK3E1Jrg/rFJQIADXELu7l3su5TAxDU/bCk9rrzCXmhEgtiq7UiCuE/1ES+teD9W9s8w8fOcXyk3I840dvuqxjRFeqdFIlEolkmpEiVSKRTD2+7wcdqSLZNxz1BVjxgnqK/AlO1Xos5xIXTXDdq6FZotzeQEGhmnAAiDjCiR20r43wsHoTogkDb1s4mzVzhIJCbtCiZ5bw/P0n+4IYiTw2GwsSfhdJdzeJaJHdGho1GJk+UuO+vcluSrKam2V7tE0hWBddD1KSL3fcV1MVFmbPqaGxtilHc1QHVYySEKnmpANIkXo17FQHfPID95Cdi/MDr3gCqqpc+iBEwq/Vs5mMnEN+hhKJRCI5LKRIlUgkU8+ob2OP3b2O1OU9kbo8GooH5W/mZK3H8Yu4qLBXQ9OLLWDgU4jMshYV4jQyEo7jtRr5tfo2saSB22yhxONUvR1ykRl0oKHkATg2E7+ic5/blap0zlKKl6gNa+iFPH6jihnVjoyT6vs+Vt8mEtfwul0m6Tg+PgXHBtVg1RLi9HJFKoi91LXtAWSChF81ys54BzeXAUDviwAvKVKvDKs/4a9vvxtNV3jha56MGX1kZdSFSAcJv7KGRiKRSKYXKVIlEsnUE9bPpDI6TrWKsSQ6UhUUFrs1iGUZR2ZYbQ05cYFk35CwhuYswhGbM9Osqjug65i9OnANRWpvcs6IahD2YwiRvelfWUdqyMJsnPUdC9LHYNShFMsFIrWA22oRSxlHRqQ6Ew/X9ogo4vUMUkLwFMZDSM+x2Z0Q0VVyCfOyz7mcjVNpDWFmGYBy4Mpup4Tbp7XrKAoMu9duh/mo4Noef/O+rzPYGfPCVz95V3ReLqmgK7UrRapEIpFMLVKkSiSSqacbiNS40wbf3x33nUvMEW2ehvwJHmwMcD2fE+WLi9Swhua0nQNgXjHZsLYwikX07bMA16wrdRQ4qU6rKUTqsEFRFXuUqxORzHu5YT8PZzEbY3swYZSYB6CkJ3fHffF9olF1t1t02gnFtukJ0dJJCCFZHHZ2A6iOzcRQlMsbJwURntQdOXQjZQDKE/EzUaOLEovh1WvEUiaWdFL3he/7fO6P7mPrgQ7P/d8fR/kxmX2fIxS14e8FiUQikUwfUqRKJJKpJ+xIjXS2gKB+phPUzzRPQuEEJ2ui+/NC9TMhYQ3NN/sJ0EzmPF8E4pRLKLUNdFNlcI1qaKyeTTRl4jZbaPmcCPvxxa/tk1aKYipCRL/wfu3FCBN+a0rQlaoY1K06al6MEUcM98g4qaHYNoKd4lZcBF8Veg3YZ/1MSFhDszpOgBahbImfr+qwhlEs4jTqoitVitR9cdenK9z3pSrf8YM3cPw7Sld0jljKQDdVejLhVyKRSKYWKVIlEsnU02mOSGRM/K11AIzFoH4mXoZhMwhN6qOpyq5TeiF2a2i2RzCzxPzYwvZs3PwsTq1KcjZ6TZxUz/MZDe29sJ9sVoT9OA7EZjnT8a941Bf2kmzXXdE5WXJ9HM9hmBEjrxHGDI9IcNKukzoSYUb16AQFhWxnC9LzbLaFk7oflnNhV+oIMguUeiLcamuwhV4sYtfqJKRI3RcP3tXgn//8NMefVuQ7XrRyxedRFIV0Pia7UiUSiWSKkSJVIpFMPWFH6mStgppK0Y669OweK8FoLHnhpN6QT1yW87hbQzOzzNxAdJQOs3Gcao1ExrwmO6mjvg0+ROMa7s4Ok0xMhP1MxB7pRtvadUOvhMXg2NNWAhSNoi3E1HZCLFea3pBRb4Lv+Vf/Yq4zo4EQ29pgB4BNY0AuOovujHCSc9R74yt2UitBwm+ku0E2mt2toXHqdeJSpF42jUqPz3zoXko3pPm+lz5uX6PXIYMv/S9OP+/5TFZXRVeq3EmVSCSSqUWKVIlEMvWI+pnYbv1MpVcBYNkOKigCkXqpUd+QlVyC1dYAf3aF+a7oSu1kdPzxmHhcuSYi1eoLcRNRJuD7DFIGAMVhFz99jK32aN/u37nkkyZRQ2W9PYH0sd1x1VpUXNcYd/F9GA2n300Nq3T0bh01HmfL3aZgivLbHU2MO+/XlU5GdPJJMwhPWoR2hXKiTHVYRS+VcOrBuG9vgu9Pv9A/TPo7Yz5++9eIJg1e8DNPQjf3P8I+uu8+zr72tUzW1hj+27+RysXoNS157yUSiWRKkSJVIpFMNa7t0W+PhUhdf1j9TH8HtAijxDHWtoccL148NCnkhnyC4cRlED/GfF/UzjSSHgAxbcKgPT50h3EUCCvTFRU6nWBKudBvYsVKTFzvqsZ9FUV0fYqu1AVKQWVK3d5Gm5nBGArX0epOv0gd9SeomoLSqqPl8zStJgVV3LstX4w7X0kA1WI2vuukMmhQjhVE+FSxgD8eEzVcPMdnPJR9nRfCHrt84nfuZjJyedFrnkIiE9n3OSZnN1i/7ZWoiQSoKnalIjqTR6689xKJRDKlSJEqkUimmt72CHxIzxrYG5u79TO6qjO/swH54zzQtPB9Llk/ExLuG24pJeK+T8ZIspkQ7mnUH+B5/qEn34bn18ddALZjQiQXBy12NBFutHAVTiqIvdT1oCs129lAV/TdGho92LE8CuFJVi/om90WVT71YZ2CImpoKrZIj70SV3opFKmZJQDKWoLqoIpREoE/EV/sRMqR3/Pjez6f/fA3aK73+IGffgL5hcubdDgXZ2eH9dtuwxuPWbrzAxjz80zWKjLhVyKRSKYcKVIlEslUE3akxhmA42AGoUlLqSW01inIH+dUXYyy3ly+vDfBYbjSGU+Mgs4bKVZNcY7IWOyoHvbIbygOQ0ezFhmhKSqzrkcNUY+zcBVOKoi9VOGkHkPtblGMF3ZraPQdkZQ8PAoitW8TTZo4rSZqLgigcj1QVB4cJVEUKGf276QuZ+Nsti3s1AIAZTT6dp/JrPg5M4MPGKRIPT9f+ssHefCuBs/40ZtYeVJ+38d7lsXZn3k19sYGi++9ncjx45hLS0zW1va6UmXCr0QikUwlUqRKJJKpJqyfiQ3qALvjvsupRWivQf4E91f7GJrCcu7iyb4huzU01iwAc0qE01oLdB2z1wCgv3O4b353ndSucDQ3IkNyRhoNWPfEiOrVjPuCELkdy8aKz4FnU4zMBk5qHrUpOmFHR6ArddSfEEsZuM0WTiYRBFCNIVnmbGdCIXllVT6L2TieD1W1CMCcK6pttgPD3hyKUXHZlfpI7vvnLf71k2s8/tnzPOW5i/s+3nccNl7/C1hf/zrzv/HrxJ/2NACM5UCk5gKRKhN+JRKJZCqRIlUikUw1naaFbqioDVE/oy0uUOlWWDHS4HtB/UyPx+STGNrl/coLa2hOdnSIZph3PTaGW+iFAsbOJnD4TuqoNyES1/F2WiiRCFveDkU9SOQdZZiNG8RN/aquEaYDNwKRVdITu+O+WnUdlCPipPZsonEdt93GSoudx+KoH9TPjPad7BuyFCT8nhmlQNUpj8T+cC0WfsAgQrekk/pQNk+1+fs/vI9jN8/y7/+3E/tO8vV9n623vIX+5z5H+Vf+G+lbbtn9mrm8jNfroY/7ROK67EqVSCSSKUWKVIlEMtWE9TNOpYISi9GIO0y8Ccte8Ostf4KT9R7HLzPZN+QhNTRji6EzRCnl0eoVFFU59K5UMaJq4LaaYo9y1KCAEKXfHCSv2kUFWMwGXamBM1tSDOrDOlq+APaEaFzfTcadZqy+TcQQLmcvKRzTwmBnryP1Cu9l6MyvtceQWaAcVNxUnRbazAxKq4qqKQy7h58GPS10GkP+5n1fJ52P8fxXPhHtMj84Opfm//MeOh/9M3Kv/hlmf/InH/I1c2kZIAhPkl2pEolEMq1IkSqRSKaabnO0Vz+zuEilG9TPjIaAwjC9wvq2xc2XGZoUsldDs8z8UOyh2tkUbnWLePrwu1Kt/oRY0sRpttDyeRrDBkXPh+gMZ7qwMHPlHakhoZN6eiLH/JnYAAAgAElEQVTGmkuuh+VY2LNCfEWj0x+c5LoeE8sRVT5AO+iBLXRr+Ol5NtrWFVf5FFMRTF1lfXsImUXyvRqqou52pbqNhuxKPYfx0Objt9+Nj8+LXvNkoglj3+fY+ZM/ofne95L50R+h8LrXPeLr5rIIsZpUKqIrVTqpEolEMpVIkSqRSKYW3/eDjtTobv3MancVgJVuA2YWObUtUnGP71OkhjU0w8Qicx0xttnPRrFrNRIzJv2dww5OsomlDJxWCzU7S3vcpmCPhbDauXL371xm4wYJU+NMT4NImtJEvKZOSriNUd2depEa7tQaQZVPI2qjoJAddRlGS4wdj/krCE0CUFWFxdlY0JW6jN5epxAr7IpUp1YjlpIiFcBzPT71gXvoNCxe8KonMVPa/4csvc9+luqv/hrJ5zyHuf/+3887JmwsLICiMFmrkMrH6LVGh14XJZFIJJKDR4pUiUQytYz6NvbYJZ2LYlfWMYJk34SRILf9oBj1rYlU3hNXMO4L0NDKzI+FwNlJafijEYmEeg2cVFGb4rSa2DPC2SyO+tiJOSzbvWL371zCrtT17aAr1RJptM24GI01lcnUj/uGz9+c9AGRkpyLZNCBpioSZY/NXrkrvZxLsLY9hJlF6G1RjpdEQnKpiFOvE89Iker7Pl/4yCnWv7nD9/zUzRw7Mbvvcwy/+lU23vBGok96Isd+8zdQ9PPvY6uRCPpcmUlljXQuiut4j/r7L5FIJNOIFKkSiWRqCetnEvoIfzLBXNqrn1Gap0VoUr2PqauXnewbEtbQVPwCM55HTDWpJR0AYoZzqCLV93xG4U7q9g5WygTEHmXPFCFHB+GkgthLPbszhPQxSkE3ai0mXpvpDqfeSbX6QZXPSIxsrxs9Crr4wGIr2MWdn7kyJxVEeNL69hA/IxJq58w0W4Mt4aQ2m8STxqNeJH39c2e55x82+LZblnj8M+f3ffz41CnWX/0ajLk5Ft/3PtT4xT9UMJeWsdfETirIrlSJRCKZRqRIlUgkU8tu/YzVAtgd912JFcCxgvqZHjcVkmjq/hJEwxqak+McCjBnpFmPietF/SGTkctk5Bzo6wkZWw6+5xPRHHBdesH4baG/TUsT3a1X25EashB0pfqZBfKdTRQUqn4HNR7HnHQZDx1cxzuQa10PRoGTqvdaKNEoW94OBVUk/K7aGYCrcqUXs3H6Y4deVIivsmKKhORiETyPiO5i9Wy8R+nI6dYDbf7xT09xw1PyfNdLbtz38Xa1SuW2V6JETBbvvBN99tIurLm0JHZSw67UltxLlUgkkmlDilSJRDK1hA6J2RadnizMsdnfZFkJnLGgfma/o76wV0Nzdz8NwJxi8mCkA0BkLFy5w3JTQ/fS9MTr24kHYT+uy5Yv3L+DCE4CIXb7Y4dRfA5j2CIfy+3W0BhDkVY7zV2pYd+s1mmIlGSrQcEXH1ictlIkTI1MbP8BPiHLQQ3NupcDoOzB2B0zCkaIo1i7zvijDc/z+YePnCQxE+H7X/541H1+UOR2Oqzfdhter8fSHXdgLhy7rOPM5SXcnR3ihrjnPZnwK5FIJFOHFKkSiWRq6TRHJDIm/kYFDIOt+AQfn2VH7FT2049hszPad2hSyEouzgPbDqTmmfc8TikN0DTMgRiLPawamlBYmc4AgGbURlc0ZjyPNXuGZEQnHbu6jtSQMOG3GXalmjNip7JQQO82gOnuSrX6E1BA29lCy+XYHm1TdBxIFKh0HeZnYvvu6TyXpWB3+fQ4DYpKOQifaqfEP6+mLXaip31s+kq475+2aK73ecaP3IQZ3d/Pqzces/6zP8t4dY2F299D9HGPu+xjjSWR8OttbRBPmzLhVyKRSKYQKVIlEsnUEnakTtYqmAsLrA3WAVgZdiE2y8meGOs8cYUidfncGpqRxbbdRivkMdtV4PCc1NB100fCua1GRhT0JCpw0sqwMHt1wupcwrHhDV84gUUtHoyrFtB3xOucZoE16tlE4wZus4k7m8LHJz+xgo7UEfNXGUC1GIj8StuG9DHKlhCl9bgYBTeG2wAMO9N7D6+EseXwpb84zdyNGW56WnFfx/quy+YbfxHrK1/l2DveTuK7vmtfx+91pa6RzkdlV6pEIpFMIVKkSiSSqUXUz8SYrK/vhiYBLLU3g2Rfkei6347UkLCGZpRcYG4gRl+9wix6Q1znsMd99YEQOOtGj4IqwpO+MUgcSLJvyGIwrnrGFmPEJUWnNqih5fNojbPB85neUVWrP9mt8hmnxRh40epC+hib7auv8omZGsVUhLXWEGaWKPWE+7xpDEDTMPrT70ZfCV/5xCpW3+ZZP358Xx+o+L5P7W1vo/eZz1D65TeRfuEL931tc3EBEF2pqVxMOqkSiUQyhUiRKpFIphLX9ui3x6J+Zm0NIxCp2WiWdPOBXZEaM7QrDhkKa2i2jXnmA/ExyibwqxtE4vqhdaWG475Gt4FiGJz1tyn6KkQyPNA+uGRfgEzMIBXVud9KAgol16Nn9/CzM2jBa55qJ7VvE03ouDs7DFJi97TQa+Ik5mgNJgci+JeycSrbQ8gsku1sYqom1VEdPZ/fdaMfTU5quzbk7r9b53HfPUdxOb2vY1vvv4OdP/5/yb7iVrIvfekVXV+Nx9GLRSZrIjypvzPGc6c3/EsikUgejUiRKpFIppLe9gh8SEZdvOEQc2mJ1c4qK8ljMGgE9TM9biom9x3YEhLW0JylyJwjxje7MyZ2rUZiJnJ44749GyOi4W830XI56qMGBcfBTc3RHTkH6qSCGFmttG1IzVEai9HIQcZEdyxUdbpFqtW3iUQAz6OXFP/kFYZtOqZISb6a+pmQsIaGmSXU7galeJHqoIpeKkF9E81QGXYPt1f3W4kvfvQUmqHy9Bc/Zl/Htf/sYzR++7dJ/4cfoviGN1zVc9hL+I3he/6hfaAkkUgkksNBilSJRDKVdML6GVsk7ZrLwkldNkStiHBSexy/gmTfkLCG5rSdo+C66IpKKwW+ZZFIaoc37rs7otpEzc7Sm/QojodYsTKwF3Z0UCzMxoTIyhyjZIk92HZSQQGiUYXhNI/79iaiygdoxVxUVLKuS1MRO7jHDiAleSkXZ6s7wk4dA9+jHJmlOqyiFwu49TrxtPmoGfet3Nti9estnvbCFRKZyGUf1/vc59h685tJPPOZzL/1rSjq/t+e9CY9fv/e32fkjDCWl5hU1kjnghoa2ZUqkUgkU4UUqRKJZCoJ33RGe5sATMpZWqMWy77oFO0lH0OtO77ifVTYq6G5ZziLBpT0JFtxIdhihnOo477RpInbbOHOiudfsDp0dOH+HeS4Lzy0K7XUE8nFjZgQdlHDnVon1fd8RgMH0xffp0Z0QtZMogMbntjBPSgn1fehoZUAKGsxqoMqRrGIHYrUR8G4r+t6/OP/d4pMIcZTvnfxso+zvvY1Nl7/C0Qf+1iOvfvdKKa572t3xh1u+/RtvOsr7+KLm1/EXFrGbTRJBvVNsitVIpFIpgspUiUSyVTSaVrohopWXQNVZSslRNXKeAiayX3jWeDKk31DVnJxvtaOgWYyr5isx0QtTEyxGPYmuIew62b1Hhn2Uxi0aah5gIMf983GsGwXKzZHsb0BwFZMvKk3GU9tcNLYcvA9n4g7BGDDHFLQhHO6OsmgKlBKH4xIBai44vtTRqc+rKMWCnidDrGEzrB79EXqPZ/fYKc65Jk/dhOacXlvL8ZnzrD+qp9Bz+dZfP/70JKJfV+3ZbW49VO3cnLnJABnOmcwl0UNjdmvo6iKdFIlEolkypAiVSKRTCVh/YxdqWDMz7NqCXG13GtB7iZONoQwuZpxXxA1NGe2LfzMInOuxylDpPxGJl3wDycQZ9S3iSUNnO3t3bCfouty1p0loqvkk/t3mi5GOD68rReJOmNmzDSbahfFMIh4w6l1UndTkseiFmbd6FJQRF/n/VaKcjqKoV39P4NhV+qpcQZQKDs2ru8yCj5MiBrO1N7Dy8XqT/jyX59h8XGzrDw5f1nH2PU66z99G6gqS3d+AD1/ecedS2PY4NZP3UqlW+E93/ceivGiEKlBV6qzvk5yNiITfiUSiWTKkCJVIpFMJd3m6BH1MwoKi601EZpU65Mwtat2HcMamkl6ifnxkNN6C1QVc9gCDr6Gxvd9rJ5NxPTBtukExlLBdTgzyXDsADtSQxaz4h5tIURCyZyhNqyjFfIY497UCqwwJVm3tlEMg3WvRcEFojOsdf2r7kgNKSQjRA2V1bYDqTLlkfiAZCct/omNMsLq24fiun+r8C9/eYbJyOWZ//HyKmfcfp/1V74KZ2eHxfe/H3N5ed/XrA6qvOyTL2NrsMV7v/+9POPYM7ghfQOrnVWMQKROgq7UnuxKlUgkkqlCilSJRDJ1+L4fdKSG9TOLrHZXmU/MEdkRIvX+ao/jpdRVC7qwhqZtzjM/2MFRfJR8FqMjqkUOei/VHru4jkcEIQy3Yx4RRSft+dw/TB/4qC/sOamrYVeqFqM2rKEXChjDHZyJhz12D/y6h80oGFM2ei20XI7t8Q5FexJ0pI4ObLdXURSWsvHdrtRy0G/bTIh7Zjp98Peez1GjtdHn3i9s8MTvOUZu/tKTC95kwtmf+znGDzzAwrvfTexJT9z3Nc/2zvKyT76M7dE2d9xyB99R/g4AVjIrnOmeQU0k0HI57EqFtOxKlUgkkqlDilSJRDJ1jPo29tgllVRwOx3MpWWR7BvNg+9B4WZO1XucuMpRX9iroamqJeaGXQCcfAajWQEO3kkN9z8NT7hxteiYghZDAe7uJq648/ViJCM6s3GD+0ciGbmItitS9SnuSrX64jlr7Rp+NoOPT37cx0/Ps9WxDsxJBVjKJoKE5EXKnS0AtuLi+uZIJCYfxb1U3/f5wp+exIzrfOcP3nDpx3seW7/0Job//CXm3/ZWks9+1r6vudpZ5WWffBm9SY87f+BOnlp86u7XbsjcQG/SozVqiRqa1TVSuSjD7gRnMn0ftEgkEsmjFSlSJRLJ1LFbP+MK0WgsLQqRqgnR0Ums0OxPrjo0CfZqaM64eeYd8SZ3OBtHqVbQdPXgRWogrMxJH4AtY0ARHT+SojLUD8VJBeGmnuwaYMQpuS7bo23UXBZtRzjG01ihEo77qq0tnBnxYUNx0MaKlbDdgxv3BRGeVNke4s8skepsEtfjbPg7KNEoel8kJh9FkXrm35ps3N/m6T/0GKIJ45KPr7/jnXQ/8QmKb3wDmRe/eN/XO90+zcs/9XJsz+aDz/sgT8g/4SFfvyEjhHK4lxp2pULQrSyRSCSSqUCKVIlEMnWESZ2xQQ2AYSnNwB6w7Iidv/ttUQVy/ABEalhD881RlrIjEoTbGQ2nViORMekfsEjdHVG1RP/rmt6l4HlM4nPAwXekhizMxjjbtiB9jNJY3N9RJoa+I1zBaUz4HfVsjIgGzRpWSnR2FgYt2kGVz8KBilSRkNyLzqF4DuVYjuqwhl4qYgT3cNg9nMqi64Vju3zxz06RnU/whGfPX/LxrQ9+iO3f+z1mX/qfyb7iFfu+3v3b9/PyT74cBYUPPu+D3Jy9+RGPuSF9jkhdWcap1Ugmxci/HPmVSCSS6UGKVIlEMnWEItVsiZHbsykhoFaGXcgscd+2cDyvpiP1XFZyce7qzmACBS1OI+njD4ck0vqhOal6vwmaxpraojAZM4gUgYPvSA1ZzJ7TlToU46m9tIEROLrTOu4bpiT3UyLVt+B61MkBHKiTupwTTm1NFQK4rKdEV2qhiNYUydNHzUn92t+u022OeNaPH0e9REpy56/+ivo730nqBc+n9Eu/tO9d8Xua93Drp27F1Ew+9PwPcePMjed9XClRIqbHWO3uhSdFx2JHWNbQSCQSyfQgRapEIpk6Os0RiYyJd7aCXiqxNhEjqcudLcgf52StRyqqU0pHDuR6y7kE9+wo+JE0c4rBRlw4MjHTPXAnNXQs9W4DdXaGvjOkMOqzHbh/hzfuG2PieIzic5SDPdSdBJh2L3he0yewrL5NNKaC69KO+6goZF2XDU906M7PXH1Hashi0JW66oiE5DnFZGuwhV4s4tc3MSLakRKpg/aYr/zNGjc8Jc/iY7MXfWz/i19k802/TPw7v5P5d7wDRd3fW4+76ndx26dvI2Wm+PDzP8xy+sJJwKqispJeCcZ9xeOM1gaartJtSSdVIpFIpgUpUiUSydQRdqROKpXd+hlDNZhrPAj5E5ys9TlxAMm+IWENjZNeYt71WI0I4RZVRgzaY3zfP5DrgBBWmqHiN+v4syLIqDDsUPNz6KpCKX1wwupcFoMx4h2jRLErxqgbcRvNs9F1fzrHffs2EUOMgDdjDlk9gQ6cGWdIR3VS0UvvUF4uC7MxFAXus8T3rOT5tEYtlGIep14nnjaxjpBI/dL/PI3nejzzx2666ONGJ0+y8XOvI3LjjSzc/h5Uc38dv1+ufplXfeZV5GI5Pvz8D7OQWrjkMSuZUKQuAmBXKqRyUXrSSZVIJJKp4bJEqqIoz1AU5eOKoswpivI7iqK8VVGUHwu+9mxFUX5DUZT3KIryuODvXqcoyi8pinK7oigpRVEMRVF+W1GUn1cU5dcO8wVJJJKjj6ifiTGp7NXPLCXm0Owhfv4EJ2sHk+wbshIk/PZiC8yNhpw0RUdq1Onh2h7joXNg1xr1xIiqu93CnhHCseg6VNwZ5maiaOrBdqSGhKnBVfIkfZ+EHmMzKt7UR3V3Op3U3gRTFeK6GhlR0ITAv89KHeioL0DU0Cino5zpeJAoUp4I186aieKPRsQSR8dJrZ3pct+Xqjz1uYtkChfekfYsi803vAElHmfxjjvQUvsbv/+njX/i1Z99NfOJeT70vA9RTpQvfsBkAPd8jBtSy2z2N7ETEbSZmSA8KSqdVIlEIpkiLilSFUV5IvAqIAH8FvAvwK8BdyiKUgD+CHgP8DHgA4qiPB54ve/7bwdawH8BXgHM+r7/buB7FEX5/sN4MRKJ5Ojj2h799phUWsNtNPfqZwzhYLUTK7SH9oEk+4asBF2pDa3E/LBNM+aAqhIZiV23g+xKtfo2sZSJ22zuhf24LqdHmUMb9YW9QKaKG3SlmhnW9S4oCqYymTqR6vs+Vt/G9IUw2TD7FH0VzCSnu9qh3MvFbFzU0MwsUg7qirppsQsbNdwjIVJ9T1TOxNMm/+4FKxd9bO0d72B86gHm3/52jFJxX9f53PrneO3fvZaV9AoffP4HKcQLFz+gV4UPvRA++nJuGHbx8an0KhjLS0wqa0FXqnRSJRKJZFq4qEhVFOU4cAPw98Ff3QJUfd8fAw7wU8AiUA3+ewbwXKAePL4KPC887mF/d77rvVJRlK8oivKVRqNxpa9JIpEcYbotC3yIK6JHVF9coNKrsIwQA6dckTJ6kCI1rKGp+AXmxxaupuBnMxgd8avuIMOTrN6EaNLAabXoJTUAio7Lvf0Ux2YOJ9kXIGZq5JMmp0YzAJTUGNVRAy2Xw3SHDKds3Nceu7i2h+kMAFjTOuRdF9LzbLatQwmgWs7GWWsNYWZpd6+3mRCj4BFlfCRE6skv16id6fJdP3wjZlS/4OO6n/kM7T/5CNlX3EryWc/c1zU+s/YZXv/3r+fm2Zv53ef9LtnoxXdeqd0LH3guNE8BcMNQjOOHe6n2WoVUPsp46DC2Dm7qQSKRSCSHx6Wc1DchXNDXAU982OP94L9z/6yc5zFK8N/D/+4R+L5/h+/7T/N9/2mFwiU+NZVIJI9KwhqJ2EiIgHYhiuM5rIxHEJ3h3o7YeTt+gOO+YQ3N/ZMcc0FX6iSXxmitAwcsUvs20aiCPx6zE/eIKToJ3+feQXJ3JPewODYb5xsDIe5LqFSHVfRCAXPSnTondRR0pBqjLug6Z5UdipMRTnKOjmUf+LgviK7Uem+MnVqg3BaJvtWEuG8Rb8h46ODa3oFf91oxGTn888ceoLic4rHfdeHRW3tri63/9itEn/hEij//8/u6xscf/Di/+Plf5In5J3LHD9xBJpK5+AEPfBZ+93ngOfDyT0B6gaVOFQVltyvV3toilRH7x9JNlUgkkungoiLV9/1bfd//YeD/Bu4BPgMsKIpiAibwJ8AGsACUEaPAnwWOBacoA38b/N3Cw/5OIpFI9k34JjMaiICNtHBGlnstEZpU7zMTNygkDybZN2QlF+fu/gzzQVdqfzaCVl0V/3vn4HbdrL5NRBPXaMYcimoE30jQ9WOHVj8Tsjgb40zbgUSBouPQtJpo+RzGcIdRz8b3Di4g6rCxApGqD3dQZmfwFMhbPQYR0aF7KCI1F4ZPlYk5Y2bMNBVDuHrmWNT6DKdM7J/LXZ+uMOhMePZPnEC5wG6077ps/p//Bd+2Ofbr70LZR1DSn5/6c970hTfx7aVv5/23vJ+UeYlpiK98CP7ox2F2GW77W5h/KhQfS6x5krnEnBCpy0vg+8TdoFZJdqVKJBLJVLDfdN/XA98J/DLwM77v14H/BPws8BLgVt/37wV+Q1GUXwbywNuADwBtRVFeC/yj7/t/c1AvQCKRPLroNC10Q0XdXEXLZln1hKO63KpA4eCTfUOWcwn+pZ0k4fukVZPtlIJb3SSWNg/MSXUmLs7YJYJ4I71lWhQ8GMXnAIWFQ9xJBbGXutEOulLHFp7v4WRT6N0GnudP1ahk6PzqvQberBA7xcEOO7qoiDmMndSloIZmwxfXKJsZNp0mWiaDMRBhW9M68tttWtz16QonvrNE+TEXdjdbd9zB8MtfpvzmX8FcWbns83/kvo/w5n96M989/93c/tzbiRsXGW33PPjMm+Gv/w+48Xvh1k9CJvgcvPBYaJ7ihswKq91VzKArNdLdEq+jJZ1UiUQimQYuvFByDr7vfxj4cPDH2x72tc8Dn3/Y3/3WeU7zs/t/ehKJRPJQwvoZ+8EK5uIiq51VkkaCXL+CnzvBybt6vPip8wd+3RvyCdoTDTdXZh6DWtLl+HBIIqXTbx+M8AjdP8MR+7ZnzR4Fx6FrBB2ph+2kZmPYrs8oPk+5dz/EwUpH0dobUA72ZRMHV9tymITjvtp2lXFa3LeCM6Hm54DDFalnnBzfBpS1GJuDKnqxiN6pAY+fWpH6Tx97AEWF737JjRd8zPBf76LxnttJ/+APknnxiy/73L9/7+/zrq+8i+csPIdff86vE9EuMgVhW/CxV8I3/xKediu84F2gnfNWpvBYcEbcEMnxr/W70J8hRKpSXceIrsiEX4lEIpkSZE+qRCKZKrrN0V79zLLoSF2OFlCAdnyF3sg50NCkkLCGZphYYM71WI8JIRmPeAfmpIbunzEWybBrWpvieEhTzaMoMJc5fCcVoGOUKAXBP92UtjuqOk1dqaHg11obWGkxclpwPdbdGXRVoZA62HFwgGzCJGFq3GcF4VO+SjUUqU0xnj7sHNz+8rVi4+QOp/+1wbc/b5nk7Pl7et1ul803vhFjbo7yW/6vy55kuPPrd/Kur7yLW5Zv4Tef85sXF6j9Bnz4B+GbfwU/8DZ40W8+VKCCEKnADb6O5Vg0zTFqKoVdqZDOxWRXqkQikUwJUqRKJJKpwfd9uk2L1KyJs1Xdq5/RhLg66R98sm9IWEPTMuaYHw150BTCLaZN6LcPxp3ZdVKHO6AoNCJjClaXTT9HKRXF1A/3V3YYzFRX85RGYpdyO+FjTsT/nqbwpFF/gqop0Niik1BQUci6Lg+OM4fWN6soCku5BA90gFiWsuPQnXShkEOrrQHTdQ8BPM/nC396imQ2wrfdsnTex/i+T/Utb8Gu1zn2G7+Olrx0aJnv+9z+b7fz7n99Ny96zIt4579/J4Z2EZe+cT/c+X0iyfcn/gCe8Vo4nxAu3AzAyliI0XDkV3alSiQSyXQhRapEIpkaRn0be+yS0Efg+ygLc2wNtlhxPdBM7umLXbnDEKlhDc0GReaGbc7GxZvdiNtjPHBwJu5VX2O0u0fZhJk0nqpQdF3WJplDH/WFvRHYdTdHxvOIqAa12ATD7gPTJbCsnk00oYNtsxP3yGoxdOCbwxTzh+hIL2VjVMKuVEvct9FsDK9RIxLTGXam5x4CfPOLm7TO9nnmjx5HN7XzPqbzsT+n+4m/ofBzP0fsKU+55Dl93+e3vvpbvO9r7+MlN72Etz3zbejqRbaPHvw83HmLGPV92cfhcT904cdG05A+xg1dMQmw2l3FXA5EatCV6vvTEwAmkUgkj1akSJVIJFNDpyHckfhkG4DtnIGPz/KwD9kbub9hkU+aZBOXnyh6uYQ1NKedHPOOQzsJKAqRURuAwQGMce6OqLZruDPCjSq4LvePUoeyQ/lwooZGMRXhgckMClA00mxEhrsidZq6UsMqH4B6dEJBMUCLcLJjHOq9XMrGWd8e4meWmBuIn9Nu2gDXJZbQpmondTy0+dJfPMj88Rlu/Pbz18KNHzxD9a1vJf70p5P76Vdc8py+7/P2f3k7H7r3Q/zEzT/BW57xFjT1/OIXgLv+CP7wRyA9Bz/9t7Dw7y79xAuPJd86TdJIcqZzBmNpCXtjg9SsgTPxpmpsXSKRSB6tSJEqkUimht36mSCp82xGuJfL7epusu/x4sG7qCEruThfH2SZd1xcTcGdTWP26sDBdKVaPRtVU1Bae2E/RcflG/3UoXekhixm43xzkAagpEVZNTqovoepuVPlpI76E6K6+PmomhZFX8FPz1PtjQ/VlV7KJRg7HsP4Mcpt8XO6nRTOXTTiTVUFzZc/vspoYPOs/3j8vDum3mTCxhvfgBqJMP/Od6BoFxGbgOd7/OqXfpU/vu+PeenjX8p/ffp/RVUu8DbE9+Hv3gp/8RpYeRbc+ilRNXMRHmz0+W//8+uMZ4+jNE5xQ3ol6EpdBtclrog9cpnwK5FIJN/6SJEqkUimhlCkmo011FSKBwnqZ7bX8HPHOVXrcXP5EEVqPsFXuynmgq7UUS6B0ToLQH/nIJzUCdGkgdtqMToiGDIAACAASURBVEiJ/by863LWzV6TcV8Qe6n3dkzQTEq+yqbdQE2niSjjqRKpVs/GVMTzrehd8rbNJF7G9fxD6UgNCRN+m3qR4niAgkItLpy7qGpPzbjvTnXA1//+LI9/5jyFpfP/f6rxm7/F+BvfZO5//A+MUumi53M8h1/54q/w0ZMf5bYn3cYbn/bGC4cr2SP4s5+Gf3gXfNt/hv/0UYjNXPT8d59t82Pv+2f+8EsV7nXmwLFYiRX2ulKBqNUEZFeqRCKRTANSpEokkqmh0xyRyJi466uYi4us9SrkIzMkPZft+AqDicvx0qVDW66UlVyC1UmGGUUnqmh0MwZ6XQTi9A/ISY0lTZxWi05CIanoRLQYXeLXZNwXYHE2zmZ3gp8+RsmxqQ/r6IU8pjucqjFJq29jekKMrBtdiuMB/UgROJz6mZBQpJ71CxhA3sxwNiIcvIg3nBon9YsffQDdVHn6f3jMeb/e/8IX2P7wh5n9qZ8i9X3fe9Fz2Z7Nm77wJv7y9F/y2qe+ltd9++suLFAHLfiDH4Z7Psr/z96bx8lVl/n+73OqTu370lW9d2cnJAECBAGRTQX0KqviBoo444yKjhdHcXTuT0edn+Ouo/fOnZFVREFlUwSVNWEPJGRPOun0vnfXvp/t/nG6OokJZOt0VTPn/U9eqTrn1LeqT1efz3k+z+fh4v8F7/13eKNAJeC5PZN88D9fxClZkCwCWytGgFqnYGesMIbaZFiVbQnjhpJZSTUxMTGpf0yRamIyy2Smisjl4w/RMTmY6ozUA8bP2IwKy169BTgxoUlVOiJuNEQq7iYasTLlBUb6keyWWbH7lnIVHC4RvVAg4VKIYqHgiAHCnNl9W4JOVE2n7G4iViogazJ6OIhUycybSqqqaFSKitFLK4qknTrRQpop0RArJ7KS2hxwIgrQXQkCEJc89FpTIIrYKhnkklr33w+9Wybp2zrFmf+jE5fv4P5uZWKC4Vu+jH3JEhq++I9veCxZlfnC01/gsd7HuPn0m/nkKZ98/Y2nuuHWt8PQBrjmNjjv5kMn+O7HH7eMcMPt62kJurj/U+ewMOrhxWwEgM6Kcb4OSFkElwt9qBenVyJjVlJNTExM6h5TpJqYzCKqrHHft9bz/P17ar2UNyWZySK+kB15aHhm/EyHbqSCbi4ZAmTJCe5JBUjZmmhSNYbdFbR8HrdPIj8bdt+sjEPSABizV2hQdVLWavXPddzHPxKqs1IztjixghEKVQm4kfLJeVNJLeWnR/mU0uh+L7ooEJUrjBICoClw6Fmfs4HNKtLod7KjYCRNx5EYKY1hjUSQCkaQUj2HJ6mKxnO/3UMg5mLlBS0HPa9rGsO3fBktl6P5B99HdLz+Z6nrOl974Ws8OfAkt6y5hY+t+Njrv3Df8/Dzi6GUho/+HlZcfdi13v1iH5++ZwOrWvzc98mzifkcLIt72Tiug7eJjqzxefdUx9D09eGdTvg1MTExMalvTJFqYjKLjHSnKBcUujeMo6larZfzpkKVNXKpMm67AoqC2hghUUrQXqmAv5Vtkwoxnx2/642tgcdDdQzNiBinsVSg35EHwOXUZ8fum5OxiYbAGrYViFZKjAlhwm4bztcZ/zHbtIaMKuOEJUosa/T8Fnw2rNkJSnl5XpzXVTFtzSdQAm4AoqpCvxIg5Lbhsr3BuJNZoC3kYlfaAnY/cU1jND+KtaEBa2Z8en31K1K3PD1IaqzAudcswnKIubyJO+4k/9xzxL58C/ZFi97wWLdtvY2Hux/m70/5ez580odff8PN98Fdl4MrDJ94HNrOesPj6rrOT57YzVcf3MqFSxv4xY1nzfzeL437GEmXkMNLaEv0IQridHhSG3KfOSvVxMTEZL5gilQTk1lkYIdx576YlRnenarxat5cZKaKoINLzQAwGTYuSttzCYgsZvdY7oRafcEYQ9MWctGrRmgq5embFqlOi3zcdt+qRdWuGVWefmuaaDnPoBqYs9AkgEa/E0GAIS1MTDHEXtojIhWN87k6JqeeKeYMEWjJjFPyGZW+qKqxp+Q7oVXUKu1h1/Ss1Dbi5RJFpQjRENapYYC6DU8qZiusf6SXtpNDtK8IH/z81m2M//CHeN/xdgLXXvuGx3q873F+tOFHXNZ5GX9/yt8feiNdh2e+A/f/DbScCTf+BUKH7oGtomk6X3t4Gz/4SxdXrW7m/153+gE3cJZNB6dNORdgm9xNi6dl36zUoSG8QTu5RAlNM2elmpiYmNQzpkg1MZlF+rcnaOjwYbWJdG+YqPVy3lRU+8icBaMaNeA3xFJHYtBI9h3PntDxM1Xawy62l0I0KgpTxqQWHHqeQrpyXBe+pWnxJ8mG8J10KTQoCnvL/jnrR4Vpu6rPQY8cIKRqWAWRKbeKbXpW6nyw/JaqldTEKDmPFRGBkKqyI++lyX/iP8vWkIvJXAXF10y8kDbWFHBhGTNCtgqZ46+6nwheengvSlnl3GsOHjmj5fMM33wz1nCYxm984/WDj4BtU9v48rovsyq6im+c+zrbKhV48FPw1Ldg1QfgugfAFXrD9VUUjc/d+xp3vtDH35zXyfeuOQXJcuBlTDXdu0doAblApys2nfDbDrKM21pGU/VZ6SE3MTExMTlxmCLVxGSWKGQqTA7kWHBqhPYVEbpfmzDv1s8i1T4y+2QfgsPBXmsSEZGWYoaEq5OSrLHkBCb7VumIuNmY9dOkqCQ9oAsC9nIKTdOPy8ZZrf5JZUPUZFwQVRR2FnxzluxbpSXoYmfBjwWIWr2MOitIlayxzjq2qlapfpbixCBpN4RFOxbRyra0bU6q0tWE34y9ifi0xTfnt2FJDINQnz2pEwNZtj07zMoLWgg1ug96fvSb36LS30/Td/4NS+D1x8GM5ke56YmbCDlC/PjCH2O32A/eqJiEu6+CTffABV+GK/8DrIfYbj/yZYUb71zP7zcNc8tly/ind52EKB4sfhv9DrwOK5srjQB0iC76Mn1YW1sBcFSSAGTNhF8TExOTusYUqSYms0TV6tu2PMyi0xsoZiqM7DEtv7NFerKIVRIRhvbOjJ9pcgSxAT00A7DkBM5IrdIRdrO7EqZJUVAtAkrAjZQzqubHU53Zv49S87pRLQINqkq/Epx7kRpysjljCJUG0cagLYdNnk8iVQYBrLkEk06FCBZ0T5xcRZ+Tz7J9OmBr3NJAvGR8bkmvgKhrOJyWuhOpuq7z7H27cbglznh3x0HPp//wCOkHHiDy93+He82a1z1OQS5w05M3UVAK/PTinxJxRg7eKNEDt74T+l+EK/8TLrjlsAm+iXyFD/38JZ7bM8l3rl7F352/8HUruYIgsCzu5bn0dMKvolJWyySjhs3bkRkBMBN+TUxMTOocU6SamMwS/duncHolIi0e2leEsUoi3a+O13pZbxqq42fkgf3Gz1gMIbWlbCTgLm6Ym0pqGjdB0YUVgULQhS1pXPjmjiPht1r9s2YmkP2GyImqKiN6iObg3CT7VmkJutibFdCdQWK6SI+UwjZTSZ0fdl+7Q0RAZ9xRpkHTKDnjwIkdP1NlZlaqFiGsqlgFC+NuBQCHXa87kdq9YYLh3SnOeu8CHO4Dg8cqg4OMfu1rOE87jcinPvW6x1A1lVvW3UJXsovvvu27LA4uPnijgfXw87dDbhyufxBOeeO+VoChVJH3/cfz7BjJ8B8fOZ33n9l62H2Wxr1snNDRvY105g1nQp+URnA4kMZ7QWBeJfzqms4rj/ay/bnhebVuExMTk+PhxEYcmpj8N0HXdAa2J2hZFkIQBSS7hfYVYbo3TnDetUsQDmFLMzk6MpMlfGEH8sAgnre9jd7My6y2RsDhZ1PCRpPfgddx4pJ9qxhjaAQKzmZiyKT8FrzjvdA0O5VUS3KUos+wPgZ1G2ncc9qTCsasVF0H2d1ETJZZJ6aRrDoCGoV5UUmt4Jh2jw5KOWKVMhmbEcgzF5VUv1PC67CyuxLi7UBM8jEoFDgdcFiVuhKpSkXl+fv3EG72sPytTQc8p8sywzd/AQSBpu9+F8H6+pcMP97wY54aeIpb1tzCeS3nHbzBtgfggb8Dbxw+/FuIHELE/hV7xrNcd+vL5EoKv/j4Gs5acHCY06FYGveRLfVTbltMZ3IQ7NCT7aOptRV1oA9P4Nx5lfA73pflpYf2zvzfF3HQvDRIy9IgzUuDuP1vbJU2MTExmY+YItXEZBaYHMpRzMq0nbwv+GPh6ga6N04w0p2mafHr93CZHB5d18lMFmlssaGXy5TjQYpKkXY5D5El7BrPs/gEJ/tWqY6hGbfEaVR6mfBodOzuQWwRjmsMTSknIwggTg6Ta7Dgx4Jsb4C8MKfpvgCt05XbrKORWLmPoq2MJRLDLlQozQeRmpWxW1QAhqQ8K0sFpjyG/XMuKqmCINAedrElZ6TOxiwOeqxGKrVdL5LIzG1l/I147fEBslMlLv/8aQf1eE787GcUN22i+Qffx9bS/LrHuH/3/dy+7XauXXotH1r2oQOf1HV47kfw+Neg9Sz4wD3gPoQN+K/Y2J/khjvWYxVF7v3k2Sxv8h3xe6om/E44Omnt+S3+BQvoSfdwQXsbld5evBc45lVFcmLAcDG856ZTSI0XGdyZYO/GCXY8Zzg4go1uWpYGaVkWpGlx4KBquImJicl8xBSpJiazwMB2ox+19aR9IrV9ZRiLJNK9YdwUqcdJMSsjl1VcGAmzkyErTEF7ehSt/WK6e3Oct/jwF76zQXUMzYAepam8jUGXkzNyWVw+6TgrqRUcHgl1aopkR5CoLpAQI3gdVnxzUCHen2rldsoSJZZ+DWwu1JAPm1qgMB/svnkZt2D8LFJunWi5yIgewmYVCbttc7KGtpCLncMK2DzENYFN2gSC3Y5NzlIoeNF1/Q0TcueCXLLMq4/1svC0KC1Lgwc8l3/xJab+73/iv/oqfO961+se4+WRl/nGC9/gnKZzuGXNLQe+J02FR78I638OJ18FV/wfkA4/AuiZrgn+7hevEvXaufvGs2gLH52or46i6qaFVjlPp7vJGEPTdhL5tevwhR0Mdc2fvICJ/ix2l5XW5SHaThZYdWELmqYzOZBlcGeSoV1Jdjw/zJanB0GAaKuXlmVGpbVxUQDJPjczlk1MTExmE1OkmpjMAv3bpwg3ew6wXdkcVtqWh+jeMM5b37fYtPweB9Wqh7M0BUC/X4Yp6MhOkHR2UFG0OelHrdIedrFrPEwjFbbZDeHsch6n3Tcn43Bb0bJZJp0eGmTZ6Eed49AkMBJSLaLAiB4hVsxA0EU54EIqZ+dHcFK2QsBqnDMZF0TzGt1ygOaA85CJsCeC1pCLx7ePoze1Eldk/iwnsTY0YCsmUeU4cknF5qztn+AXHtyDrsE5Vy864HElmWT4S1/C1t5O/Ctfed39e9O9fP7pz9Pua+d7538Pq7jf+1HKxvzT7Q/B2Z+Bd3wDxMPHYDz02hBf+M0mFjV4ufPjZ9LgPfq5tn6nRJPfwaZSjAuATquXdem92NouQa9UcDtUcqkyqqxhkeo/mmOiP0u0zXvADQBRFGho99HQ7mP1Je2oisZYT4bBXYZo3fTEABv/3I9oEYh1+mYqrbEO/7x4zyYmJiamSDUxOU7kssrInjSnXHRwoMei0xvo2TTJaE+GxoX+GqzuzUFVpDpSg8iSxB5bCptoJa6obBSmk33nyO4LRnjSlp4A77QoPDPtQnRKCpnjraRO3+MYtZWIVAr0K0FaGubeGmq1iDT6HfQoAd6pGrbZvE9C6kvUfU+qrumU8gqSM4vmc6NaykRVhcdKPpoCRy94jpX2kJuKqlH2NBMv9qPYFIiEkLITYD+JQqZSU5E6ujdN10tjnH5pO77Ivhshuq4z8tV/Rk0kaL3314iuQ59/6XKazzz5GSyChX+/+N/x2vb7/Stl4Ncfgt518M5vwjk3HdGa7niuh6//YTtndoT4+UfPOC4HwdK4l3XJCJ8DOjV4oDiJ3Gm4LVxqBnTIJkoEYvVjvT4UqqIxNZw75N+X/bFYRZoWBwzXzv/oNP4udacY2pVkcGeSV/7Yy/pHerFKIo2LAzP9rNE275zduDExMTE5GkyRamJynAx1JdFUndaTDx5E37EygsVqpPyaIvXYqYpUaaQbobmZvtwAbbYgIrC1EgdKLJrDSmpH2M1aOcxHFYWp6WtzBwWGk8d+sVfKyfjd032UtiyrFIXdJd+chyZVaQ266CoG+LCiIiCQcgtIhWTdV1LLBQVd05GKKSp+F1CmQVXZlvOwrGnuPstqwm9KihNPvAYhF+WQG8vgCDQas1JrJZB0TWfdfbtx+W2svrT9gOeSv/oVuSeeoOGWL+FYvvyQ+8uqzOef/jzDuWFuveRWWr37CajsGPzyahjfYYyYOYIEX13X+eFfuvjJk3t45/IYP/ngaTik47OoLo37uHXPJHowRkfRcDuMBkECHPlxIEBmqlj3IjUxnEdTdKJtR3cTTrJbaFsepm25ETZVLsgMdU2L1l1JXnigGwCb00rzkoARxLQsSKjRXXMbuomJiQmYItXE5Ljp354w7k4fQoTanEYfUffGcc69ZpFp+T1G0pMl3H4balcvUnsbvZleFiGBKPFq2kdrSMBtn7uvs46Im0E9SpOikpi+drSX08hlB5WickwVsmJWJuo2+j2nXDpRRWWTHODsGth9wehL3TzuRQLCVieTLoUGOYtc1pArKpKtPvvcZkb55KYoeG2ICAQ12FlwcvEcCv6qSB0VG2gspgEXeb8d68beGZFaK/ZsGGe8N8PbP3YSNse+c7XU1cX4v30H93nnEbr++kPuq+s633jxG6wfXc+/vvVfOa3htH1PTnXD3VcZI2Y++GtY/I7DrkXVdP75oa3c81I/157RyreuXIHVcvx21GVxL7KqU/AvpjM1AjbotWdZIknYEwNAYF7MSq2GJkVbj88pYndJLDg1yoJTo4Bx/lUF6+DOBD2bJgFweiValoV4y+ULDqiwm5iYmMw1pkg1MTlOBrYnaFoSxPo6d/4XrY7Su3mSsd4M8QVmNfVYyEwU8UWcyI/34zx9NYPZl7lYCEB4IbvGiyxpmDurL0Bn2E0ZG35LENUiUPG7sBcmgRi5VJnQUYpUTdMpFWTsmlExTruhoaQyoofnPNm3SkvQxe9yLnSnhZhgZ9hZomVmVmoFKVyfF7DF3PQon/Q4Kb9IWJDA1YBctM5Jsm+VpoDR19uvhnmbYlTIU16RWHYCgELm2K3hx8umJwbwNzhZsiY+85hWKjF8882IXi9N//+/IrxO/+gd2+7ggT0P8Ler/pb3LHzPvieGX4NfXmOEJX3099ByxmHXUVZUPn/va/xxyyifumAh/3jJ0lmr4i2dTvgds7fTOvAg1rYYPbk+Tm5txTLUjWhZRXaq/hN+J/qzSA4L/ujsnrsun43FZ8ZYfGYMMNwqQ12GaN29fhxf2MFbrlg4q69pYmJicjSY3fMmJsdBZqpIaqxA2/KDrb5VOk6JIloEujeMz+HK3lxkJot4fSJaPk8h5kPRFdrzSbTwEvZO5uZs/EyVpoADqyiQszUS0UWyQTtSahSAfPLoxUcpJ4MO0rQITLshqqqM6KHa2X1DTjREFE8jDbpAv5TFJldFav0m/Jaq82anhkm4NCK6QMnRAMzNjNQqVotIc8BJVzmEX9NwiBITbhVJziMItaukjvakGevJsOrClgOcHWP/9m+Ud++h6dvfxho5dFL2E/1P8MNXf8glHZfw6VM/ve+JvU/DHe8GqwM+/qcjEqi5ssINt6/nj1tG+eq7T+KLly6bVZvpwqgHqyjQpbcgyXnaXI30pHuwtbUhD/TjCTnmRyW1P0u01XvCXTi+iJOTzmniHTecTLTVw0h3+oS+nomJicnhMEWqiclxMDN65g1Eqr1q+d0wga7rc7W0Nw2qrJFLlXGJRtVjImRUKTtSo6RcHciqzpLY3PWjwr4xNEM00KSqJH0C1ok+gGOalTpjUS2lUV12ZKtASLOQwlOTdF8wKqkAeUcjMblCj5RCqhi9ffXclzrzWWYnmXQqNCgKaWnuRSoYlt/NOS8CELe6GXaWENBxOGonUjc/OYjNYWHZ2Y0zj2Uff5zUr35N6IYb8Lz13EPut31qO19e92VWRFbwzXO/iShMXz5svR9++T4ItMGNf4boksOuYTJX5oP/+SIv9ST4wftP4RPnLZiV97Y/NqvIgqibjUWjWtxh8xsitb2NSn8/vrCDzFR9i1RN1ZgazB11P+rxEl/oZ7w3g6pqc/q6JiYmJvtjilQTk+NgYHsCT9BOML4vfKMyOIhWKByw3aLVDWQTJcb7snO9xHlPZqoIOrjkJAADfqNS1l4p0y+2AHOb7FulPexijxylsVxk1KVgGd4LHNsYmmr1T8pNUvYbQsomhnFIFkJzNNfzr2kNGetISg3EijmGrVlsWh6o70pq1e4ryTlGbAWi5QITohEeE/fPXbovQFvYxZakDawOYljpsxu//w6rWhORmkuW6X51nJPObZrpRZVHRxn5yldxnHwyDZ//h0PuN5Yf46YnbsJv9/OTi36Cwzr9Ob70n/Dbj0PTarjhj+BrOuwaBhIF3vcfL7B7PMt/XX86V61umbX399csjftYmzRuIHbqVvqz/VhaW9CLRTxu6t7umxwtoMjanIvUxoUBFFljsj83p69rYmJisj+mSDUxOUY0VWNgZ5LW5aEZm5pWKNBzxZWMfutbB2zbsSpiWH5fNS2/R0vVkufIjIAo0uVI4bU4CGoa2+Q4osCcJvtW6Yi42VYI0CQrDLgKCJkkdpf1mETqTB9lapy8RyKkiySFCM0BZ82SNhu8DiSLwKgeJlZIoQsCkscQNvVcSS1lZawSWDSFIVueaKXEsBYk6rUfd2Ls0dIWcpEsKqi+FuKqxh6LMefXToliDUTq1rWDaLrOygsMYairKsP/+EU0Wab5+99DsB18Q6QgF7jpyZvIyTl+etFPiTgjoOvw5Dfh0X+EpZfB9Q+CM3jY1985muHq//M8U7kyv/zEWVy0LDbr73F/lsW97EhLaO4GOksFFE0hEzVuKLqFAsWsTKWknNA1HA/7hybpsjxnTpxqCOBId2pOXs/ExMTkUJgi1cTkGBnrzVIpKjMR/wC5tWvRcjkyf3gENb2vp8fhNhITuzeOm5bfo6Q6fsY23oPU2EhvYZAOqwcBeCUXpi3kmnPxAcYYmm45QqOiMO4xbHFut3Bsdt9p0WdJjJBxC0RVlWEtNGO5rQUWUaA54KRPDRGXp99TwIGoK3UtUou5Cg6b8TuWdgtEVYXeSmBOQ5OqtE8n/BZczTSWiwwrU4g+L3YlN+eVVKWism3tMJ2rIjMhPFP/9V8U1q8n/tWvYuvoOGgfTdf4yrNfYVdyF99523dYGloKqgK//yys/S6cdh28/xcgHf6zfaU3wfv/4wUEAX7zd+dwevvrt0jMFkunHRY53yI6M0Zg1VDA+F11lI1WjWwdW34n+rNYbSIea4FdZ72FrrecTd/1H2X0X/+V1O/up7h1G1p59gO43AE73rCDUbMv1cTEpIaY6b4mJsfIwPYpBAFalu2rIGQefQzB5UIvFEg/9DCh66+beW7h6ihP/WKKif4sDe2+Wix5XpKeLGKVRIShPUjtbfRl+jhdBXwtbB5X5jw0qUpHxE2/3kCTopDwGtVOp6QeVyVVHB9karlIVC7TK/trluxbpSXooisT4OzpdNqS34FdK9S93dcuGtWxtBuiBY3nFB/NLXNr9QVonRapSSlOPLMT3WeHSBipnKZQqaDr+pxVyrvWj1HKy6y6yJhpWti4kYl//ym+d78b/5VXHHKfn2z4CY/3P84Xz/wi57eeD3IRfvcJ2PkHOO8LcNFX4QjW/+TOMT71yw00+Z3cdeOaObv5Uk34HZba6Rh7BJoC7HFkiFit2NPDwGIyUyXCzXPvxDgSJvqzRFq8lF7biF4o4HnnO5HHRkn95rfoxWmrssWCrbMDx9Jl2JctxbFsGfalS7FGo8d1bjUu9DO4Mzmn56iJiYnJ/pgi1cTkGOnfnqChw4fDLQGG1Tf3zDMErrqS4tZtJO+9l+B1H5n5A7/g1CjP/HIX3RsmTJF6FGQmiviiTpT1/dje+XZG8utpV5xo4UX07ipw2YrGwx/kBNAZdjNGkJgKk9M/TodQJJE6+q/VUraC3WVFTycZd7hpUBT2VgI1C02q0hJ0sm3ES4NmiNScT0LKZOq7kpqtYMO4UZB2QTSrsLXo5sIafJZtYUOMjQhR4oU0+BqQQx6k7CSaewnlgjLz/XEi0XWdzU8OEG720LwkgJrNMvyFf0SKx4l/7f87pAh5YPcD3Lr1Vt635H185KSPQDEFv/og9L8Al30HzvrkYV+3JKv87Kk9/O+nu1ne6OOOG84k7LGfiLd4SFqCTjx2K11aM8vKGcL2Tnrz/ZzX3IQwthdYPOPUqDd0TWdyIMeysxspbX4KQZJo+t53EW02dFWl0t9PedcuSjt3Ut7VRWHjBjKPPDKzvyUYNETr/uJ1wYJDWroPReNCP10vj5GdKr0p5qXqus4ffrqJpWfFDxi9ZGJiUr+YItXE5Bgo5WXGezOc/q6OmcdyzzyDXirhvfRSHCevYOQrX6H46qu4zjDGMTjcEs3LguzZMM5brlhg3p0+QjKTJbx+K2oqRW6697QjM0Gq9RxUTWfxHCf7VmkKOBBFC25LhOT0EuxyhmLWjqpoWKxH3k1RzMk4nMb2I/YiCxSVjXqIM2tcSW0NuXgk78fp0PGLdhJuHWksVbNk2iOhlJNxK3kUtwPFqtCgqgzI/prYfX0OiaBLokcJc6ZiVHcLASfWvlFwQyFdmROROtSVYmooz4XXGWNeEnfciTw0RPuv7sHiPdiJsH50Pf/y4r/wlsa38OWzvoyQHYW7r4LJ3XD1z2HlNYd9zWe6JvjnB7fSnyhw5WnNfOOKFXjsc3vJIQgCS2IeXi3Eel5I9QAAIABJREFUeC/QaQ9Nj6FpRx7YjbXlMrJ1OoYmNV5ALqtE2zwU/7gF+0knIU4LTMFiwd7Zib2zE9+ll87so6bTlHbtorxzF6VdOynv3EXynnvQK9O/r5KEfcECHMuWYl+ydEa8WsPhg14/vjAAwEh3+k0hUlNjBfq3JbBYRVOkmpjME0yRamJyDBg2KA7oR808+hiWaATX6aejr1zJ2Le/TfLX986IVDBSfp+6eyeTgzmirbWxqc4ndF0nPVkkFjYuzibCVqhAezHLgMWwLdYi2Rf2jaHJanGclglKPgl7IQF6lHy6jC985Bd2xVwF+3QfZcoNDarKqB6u2YzUKi1BJ1lcqDYvMdHGhFOmoZIlk6nPC3td1ynmZCQtS9nvQCSHTwpSxlYTkQpGeFJXKcB7pi3TaZ8F99QQNEMhUybU5D7ha9j85AAOt8SSM2NopRLJe+7Bc8EFuE477aBt+zP9fP7pz9PqbeX7F3wfKdELv7gKign48G9g4YVv+FpjmRL/8oftPLJ5hAVRN/d84izOWXTouatzwdK4j6c2B/k60ImNv6R7sLVdQnHDBnynOI308DqkGpoUaXaT2LoV/5VXHnYfi9+Pe80a3GvWzDymKwqV3t4DxGv+hRdJP/Twvv2iERxLlxnidekyHCctI9i5AJvDwkh3mqVnzX9RN9RlhEBN9JsJ+yYm8wVTpJqYHAMD26ewOa3EOgyBpOXzhtX36qsRLBYEpxP/5ZeTuvdelK/8E9ag0bfaeWqEp+8xUn5NkXp4ilkZpazi0jIADPhkmIR2WeH3chyLKLAgeuIv8l+P9rCLnpEITcoImYADV2YUxKXkU5WjE6lZGbfFqLSlXEZw0ogeojlQu+Ak2DcrtehsJKbBkL1Is5ylmFPqsldNLquosoa1nCTvkQhjpeyozYzUKq0hF68N+HHrOl7RxqRHI1gyxikV5sA2nZ4o0rN5ktMvbcdqs5C893eoySShG244eNtymk8/8WkEBH520c/wje+GX14Dgggf+wM0HSxqq6iazl0v9PL9P3dRUTVufscS/vb8Bditcx9qtj8nNXr51csu1FCEjkqZVDmF0hxFy+fx+Cwz6eH1xkR/DotVxF0YZbJQwHnKqmM6jmC1Yl+0CPuiRfDud888riSTlHfupLRzl/Hvrl1M3fkSyEa/eeO3vklswUJG3yQJv8O7jfeRS5YpZis4vbUZ7WViYnLkmOm+JiZHia7r9G9P0LIsiGgxfoVyzzyDXi7ju2yf9Sp47fvRZZn0/Q/MPOb02GheEmDPBjPl90io9os588bonl2OFA1WNy5d55VchI6wq6YXwR0RNztLIRorZSY9OtLEAHD0s1KLORm7Pt1H6YaQKpC1+Gjwzl3/3qFona7kpqQYDZUy/bYMtkoWTYNKSa3p2g5FaTqAypqdNEKTVJ2UNQrUTqS2h11sSdvRLTbiooMRRwlbxajmFNInXqRueXoQURBYeX4LuqaRuOMOHMuX41pz5gHbyZrMzU/fzGBukB9d+CNax3fBne8Buxdu/PMbCtRNAyku/9mzfP3321ndHuQvn38bN128uOYCFfYl/GY8C+nMGYm+U2HDYu22lslMFevyu3iiP0u42U1l+1YAHCtXzurxrcEg7rPPJnzDx2j6t2+z4MEHWPbqK3Q+9BDWWIzc2nU0LvQzNZynXKjfoLQjQdd1hruSuAPG92m1Sm1iYlLfmCLVxOQoSY0VyCXLtC3fN0Ih8+hjWKNRnKtXzzxmX7wY5+rVpO67D13TZh5fuLqB9HiRqaH8nK57PjIzfmaqD2tDA3vLQ7QLNrD7eWXKVjOrb5XOiJtuJUKTojLiKmMZ7QEglzzy6oyu6ZRyMpJqnA9pN0gEaAq4EMXaViqjXjt2q8i4ECFWytFnyyDJOYCazPk8HPvmzY4x5VKJKmXGhTBOyULAdeJ7Pw9FW8iFrAkonmbimkC/I49VKSCKJ37ebKWksOO5YRae3oA7YCe3di2Vnh5CN3zsgCq4rut868Vv8dLoS3zt7K9x+lg33HMthBYYAjW88JDHTxdl/tdDW7nifz/HeKbMzz60mjtvOJP2cO3cDX/NsriRajYktdM51Q/AgN9wLTjVNHJJpZyvr1mpuq4zOZAl2ualuGkzos+Hrb39hL+uYLPhWLoE91vOovDyy8QX+ECH0Z7MCX/tE0l6okg+XWHlBc2Aafk1MZkvmCL1GMmnynV599XkxNO/zbgb33qSIVLVXJ7c2rV4L7kEwXJg5SD4gWup9PVReOmlmccWnBpFEKB7w/jcLXqeUhWp0vBubG3G+Jl2WTaSfROFmo2fqdIedjMwPYZm1KNiSY5isQpHVUktFxV0TcdWzqDYJSqSgKyFap7sC0bwTHPQyYAaIl7MknIzUwWsx4Tf6pqsmQnG7GWi5SJDapDmoLNm1uTqGJq8s5G4XGGvNYkAOCT1hFdSd74wQqWksuqiFgASt9+BNR4/IGwH4K7td/G73b/jEys/weUTg3D/30DrW+CGR8B7cD+irus89NoQF3//Ge5+sY+Pnt3BEzefz7tXNdadBdzvkoj7HOxUmmgspLCLNrocKRBFHLkxgLrrS81MligXFEOkbtmCc+VKBHHuLtdca9agJpMEtUkEUZj381KrVt/OVVF8UacpUk1M5gmmSD0GBncmuPOfnp9pxDf570X/9gSBmGsm8TD39NOG1ffSSw7a1nvJJVj8fpL33jfzmMtno2lJgG7T8ntY0pMl3H4bWl8PtDSSLCfpyKVIuzvR9X1WvlrRGTZmpTYqClNeEAC3x3JUInVGWBVSlHw2wjoMq/UhUsHoS91TCRBTVVSLgGQ3XAH1OCu1aveVKjlG7UWiqkZPpTbJvlWqVcVJa5x4KUuflAZRxC6UT2hKsq7pbH5qkFinj3inn9L27RReeonQdR9BkPZVlZ/qf4rvv/J93tH2dm6anIQ//ROc9F74yO/A4T/ouD2Tea679WU+9+vXaAo4ePgzb+Vr7z0Zr6M2leojYWncy8v5GBagzRFmb74fqakJ26RRWa23vtSqiAo32Ch3deFYNbtW38Phmg5eqrz2CpEWDyPzvC91uCuF0ysRbHQRbfWaItXEZJ5gitRjIL7Aj91pZctTg7Veiskco8oaw11JWvez+mb/dLDVt4pot+O/8kqyjz+OMjEx8/ii1Q0kRwskRkzL7xuRmSjiDdlRJibITgckteeSDEnVZN/ajJ+p0hRwkBc9RHSJKZ9RQXLaVXJHI1Jn+ignyHosRGWZnkqA5hon+1ZpDTrZlvcRmx6hIriNGytzEfpztFSFs03OkXJDVFXYVfDSHHDUbE1xnwPJIjBMhHghjSYKCKEAdrVwQj/Dvm1TpMeLM1XUqdvvQHS5CLzvfTPb7Ezs5EvrvsRJoWV8K5lHfP4ncMbH4X13gHTgZ1aSVX74ly4u+eFaNg2k+MblJ/PAp85lRfPBQrbeWBb38kzSSGLvFJ30ZnqxtbUhDe8G6q+SOjGQRRQFXKk+UFWcq44tNOlYkVpasDY2Ulj/CvGFfsZ6Mqiqdvgd65Sh3UmaFgUQBIFom4fMZIlSvv5uspmYmByIKVKPAavNwvK3NtGzaaLu/riZnFiGu1Moskbb/lbfZ9bivfTSGTvW80PPM5Ibmdkn8P73g6KQ2i9AqfPUKAjQ/app+X0jMpNFPA4joGcibFip2xWZXUojkkWgI1Lb3jdjDI0bSY+QmC7qOoXyUVVSS9l9fZRJp0aDqjCih2aSdWtNS9AYoRKbHqEiewyxWo9231K+giiCRS2Rdgk0KCq7ir6aVqUtokBL0EWPHCY+LfSVkA9bOX1C7b6bnxzA7bexcHUD8ugomUcfxX/N1Vh8Ro/mRGGCzzzxGXw2L/+elnFuvhcu+DK8+wcgHti2sG73BJf+aC0/fmI3l62M88QXzue6szuw1Lhn+khZGvcypnpQHGE6FZXB7CCW1hb0vj3YXda6m5U60Z8l1OxGng5NmmuRKggC7jVnzvSlKhWNqcHcnK5htshMFsklyjQtMea+NrRNn/9meJKJSd1jitRjZMX5RgP+1meGarwSk7lkYFsC0SLM/MHLPfUUeqUyk+o7WZzkU098iq+/8PWZfewLOnGdddYBAUpuv52mRQG6N04c/CImACiyUZF0YVwc9fkqWBBokRVezTfQGXEjWWr/FdYedpFWGyhMF3XtapZ8qnLEVu5izhAqlqlhJp0yUUVhVK8fu29ryMmoHsStg0uwkvMKWNViXdp9i1kZu6QhYARQRVSVUT1UU7svGOFJ24t+GqeFfiHoRMpPUcxW0LTZt/wnhvMM7Eiy4oIWLBaR5N13g6YRuv56AIpKkc8++Vky5Qw/TSs07HnKEKcX3AL79ZSOZ0t89lcbue7WlxEEgbtvPIsff+A0Gry1q0wfC0vjxh2klGcBHbkUqq6Sj3nR0mm8Qamubjbrus5Ef5Zoq5fi5k1Izc1Yw+HD7zjLuNasQU0kiFgNq+/IPO1LrfajNi02xsBF24xzwbT8mpjUP7W/wpuneEMOFpwaZftzwyiV+hvFYHJi6N+RoHGRH5vDGDGceewxrLEYzlNPBeD33b9H1VWeG36OrmTXzH7Ba9+PPDRE/rnnZh5buLqBxHDetPy+DtmpEujgLE0BsMuVptniQhIlnk+4a57sW6Uj4mZPJUwYlaLXhr2YRFW0I7aTVcWeZWKIMUeFBlVlWA/TUid235agCwUrZWcDMUEi4daxVbJ1WUkt5mTsgvF5ptwQEl0UcNSFSN2Y2WeZzvqsWNNj6Pq+PtrZZPNTA1gkkZPPa0LN5Uneex/ed74TW4th/b17+91sndrKt3Mqy0a2w/vvhDNvnNm/OvP04u89w2NbR/mHty/m0c+dx1sXR2Z9rXPBogYPFlFgwNJOZ9Jo0xkPGdVij12tq57UXLJMKScTbfNS2rxlph+1P9PPQHaAsnp0462OlWpfKjs24AnZ52140tDuFHa3lXCT4bpxeCS8IYcpUk1M5gHWWi9gPrPywha6N07QtX6M5ec21Xo5JieYfLrM1GCOt1yxAAA1lyO/bh2BD1yLIIrous4Dex5gaXAp/dl+7tp2F9986zcB8L797VhCIZK/vhfPeecBsPC0KOvu66J7wzihd3fW7H3VK9ULR0dqEEswyB55mHZNRAt20jskc9UZ9SFSOyNu9igRmmSZlNeBKzsGwiLyqTJOz+EHxhdzFSS7iKjJpN0iy1WVcULE/fVRrarOSs3YYsS0EmPOCpGxDIVkocYrO5hitoJNL6I4JVRJxyYZoqrWVem2kIt7Sl4kl5WQaGfKoxNLj0ELFDIVXL7DnydHSikvs+vFUZasieH02Ejc9Wu0bJbwxz5qPK+UuHvbnZxb0bkoOQ4fuR86z5vZf8tgmq88uIXNg2neuijCN65YQWeNbfXHi91qoTPiZrvSyJX5FES99PkqhAEneQam7OiajlAH9uWqeAr6NbJDQwQ//GGGckO858H3oOmGEydgDxBzxWhwNdDgaiDmjh34f1cMn813XEnLM32pL6+nccUqhvek0XW97tKbD8dw13Q/6n4/22ibGZ5kYjIfMEXqcdC0OEC42c3mpwY56Zz6i943mV0GdhijZ9qWG9arGavvpZcBsHlyMz3pHr5+ztfZMbWD3+7+LZ9b/TmiriiCzUbg6quYuu125LExpFgMd8BO40I/3RvGOdMUqQcxM35mdA9SWxt9mb2cUaqQ9S4Fah+aVKU97OZJvYEORWXco7J4chCiRkUk0nJ4IV3MyjgcxndH2g1hVcDmjdaFlRkg5LbhlCxMiFFilT0M2fI0yTkK6fqpPlUp5WS8Sp6i105YL5O1GOOeai3428IuVCzIrjhxrIy6KrRWjPO7kCkDs3cub392GEXWOOWiVnRVJXHnXThPO23G7fHQnodIVNJ8PJmEjz0KjUa/Y6Yk84M/d3HXC72EPXZ+8sHTeE8djpQ5VpbGvbzY18CHdZ2YLcBOZ5LVgoCzNIWqxClkKrgD9lovk4mBLIIA7sk9ZAHnKatYN7oeTdf4h9X/gKIpjBfGGSuMMV4YZ9vUNhKlxEHHcVgcMwJ2f/G6v5iNOCNYxUNfBlb7UnPrniV++f9k9yvjZBMlfOH6cHgcCblkicxkiZUXtBzweLTNy97XJqgUFWxO8zLYxKReMX87jwNBEFh5QQtP/3IXI3tSMz0PJm9OBrYncHolIi3GBWXm0cewxuM4Tz0FgAf3PIjT6uSSjks4I3YG9+66l1/t/BWfXf1ZwAhQmvqvn5P67W+JfvrTgGH5ffa+3SRH8wTj87taMdukJ4tYJRGxqwvtlOUUlW105FIMNxjJvrWekVqlc3pW6jmKwqhH4aS+HohyxOFJpVwFu2RUSNJuAUnz0hSqn3NBEARagk4GtTCx4kaetcE5cpZsTqn10g6imJMJltJkPSJRRWWMMDGvo+aCv216VmrG0URcSTFoL3BuxTg/irM4hkZTNbY8PUjz0gDhZg+Zx/6EPDREw5e+CICiKdyx9TZWlGXOXHolNK5C13Ue2TLCv/x+OxO5Mte9pZ2b37kUv7N+R8ocC8tiXu7YHAEHdFjddBf6scbj2FNDQJzMZLE+RGp/lmCjG3n7i2Cx4Fi+nA0bHsJv93PDihsQhYPP5YpaYaI4sU+85veJ2PHCOK+Nv8Z4YRxZO9BaLgoiEUfkgIpsg6uBRncjF7ZeiGvNGtIPPUzYZlQdR7vT80qkVscENi858Npspi91IHvQcyYmJvWDKVKPkyVnxXnhgW42PzVoitQ3MbqmM7AjQetJIQRRQM1mya9bR/BDH0IQRYpKkUd7HuUd7e/ALblxS24ubruYe3fdyydWfgKX5MLW2or73HNJ/ea3RD75SQSrlYWnRXn2vt10b5jgjHfVjzCpBzITRbwRB8rICOV3GP1RbZUyu7UmbFaR9lB9pN82BRyMCsas1Je9AtbEEAgc8Ria/fso0y5QCv6a21P/mtaQi71jQWLWClNuDVslS6mso2k6Yh1YJAFURaNSVLAWkiRdOlG5zIASrItRPq3T5+qkpYF4eZhXJQFbxfiZ52dRpO59bZJcssx51y4BIHH77UitrXgvvhiAx/seZzA/zP9MpRCu+DR9U3n++aFtrO2aYEWzj/+6/gxOaQ3M2nrqiaVxL1P4UOxBOlV4JN2LrW0JttFu8J1OZqpE46Jar9IQqa3LQpSe3YJ9yRJEp5ON4xs5LXraIQUqgM1io9nTTLOn+XWPq+s6yXJyRriOFcYYy+8Tsv3ZftaPrSdbMQTp51Z/juvXGLO/7b2vIdmbGelOs2RNfNbf84lieHcKm9NKuMWDrusM3PgJ/FdeQfR8431N9Jsi9WgZzQxgFW1EPLFaL8XkvwGmSD1OJJuF5ec28doTA2QTJbyh+ugjM5ldJgdzFLMybdPzUXNPPYUuy3gvNf7YPd73OHk5zxWLroAn/gU638ZHT/4oj/c/zoN7HuRDJ30IgMAHrmXops+SW7sO70UX4gk6iC/w071xnDPe1VGrt1eXZCaLeF2ArjMeMi7OOmSFPxWiLIx6sNaJHdZqEYmFAvg0N1M+EHUNp8tyxJXUYlbGazWsn1kXJJP1M36mSkvQyY5eH1c7FVIhYw4pCJRy8qz2Ux4P1QAia3qCyZCRkry34qepufYi1WO3EvHYGNSjNBYzDDkFLKqKRdQozKJI3fzkAL6Ig45VEQobNlLctInYV76CYLGg6zq3bfk57YrGBfGz+ckWKz99ai02i8jX3rN8Xo2UORaWxX2AQMK9gM5Clqwli9bcgOWZl2DlvvaCWpJPlymkK0RaPRS3bMF36aVMFafozfRy5eIrj+vYgiAQcoQIOUIsCy173e2KSpHrH72eZ4ee5cYVN2JtbKS0fj2xzpPmXcLv8O4UjYv8iKJAaccO8s8/jyBJtL7nPbgDdrMv9SjRdZ2/uf+9uESJX1/30pumFcCkfqmPq7x5SKmra2acyIrzm0HX2bbWHEfzZqV/u5Ew2zI9HzXz6GNYGxtxnrLP6tvqbeUMRYB134dHvsCpkZWsiq7iF9t/gaoZCdDeCy7AGo2SvPfXM8deuDrK5ECO1Hj9BdHUCl3XSU+WcFuMz6TPV8EuWIipKs8lQ3XTj1qlI+IGJcTUtAPZ5dDIJw8vUnVdN4KTlDyqVcRtVRnTw3VR/duf1qCL7nKQmKpStglYBaMftZ4SfqujfKyZiZmU5F0FL02B+rhx2Bpy0V0JElcVck4QbBIOUZ41u+94X4aR7jSrLmxFFAUSd9yB6PMRuMoQNy+MvMCO5C4+lkryZOAafvCXLt6xPMYTN5/Px87tfFMLVDButLhsFvrENjpTwwCkoy6YGsflk8hM1b7HuiqaArYcWiaD85RVvDb+GgCrG1bPyRqcVifnNp3LpvFN5OX8vnmpC/0khnJUivVn8z8U+XSZ1FiBpsXT4+LWPQtAcdMmdF03w5OOgR39z9ArKGzXi7yw6/5aL8fkvwGmSD0Gcs89R897Lyf3zDMA+CJOOlZF2PbsMIpsjqM5UgZ2JubNH7yB7QnCLR7cfjtqJkP+2WfxXXIJgigymB3k5dGXuXzh5Qiv3G7sMLUbdvyejy7/KIO5QZ4aeAoAQZIIvO8a8mvXIQ8ZNzUWrm4AoHvDeE3eWz1SzMooZRWnbPQU7XKmaRPs4G1kd5q6GT9TpT3sIlGOkppeltNSOSK7r1xS0RQdWylN3mudHj9TPzNSq7QEnQzpYWLTcz4Fu1G1rC+RaqxJknMzM1IH1CAtdfJZtoVcbC0EiCsKCAJqyI9NK85aJXXzk4NIdgvLzmmkMjBA9vHHCV57LaLbaCO4bcttRHSB/+Fo5Vs7Gjm9PcjPPrSamK8+RPyJRhQFlsS8bJUb6cwbFcHRaaenxwXZOqikTg4Yosk9sQcAx8qVvDr+KnaLneXh5XO2jnOazkHRFdaPrt83L9WZR9dhtGd+VFOr81Gbp9uw8uvWAaCmUsh9fUTbvCTHClRK8+MapB740+bbseo6YVXl1o0/rfVyTP4bYIrUY8C9Zg3WxkYSt90+89iqC1so5WR2rzeFxpEwujfNwz96jecf6K71Ug5LpaQw0p2esfpmn3wSXZbxXXYpAA93P4yAwOUt58PW38Jp10FoITz7Ay5uvYhmTzN3brtz5niBa64BQSD5m98AxszdWKeP7g0Tc//m6pSq9c6RGUH0eNilDdMhK+S8C4H6E6mdETf9ahSry3BXONTcEdl9Z6p/+SQZlyGsRvVQ/VVSQy5SePAIdmyIKI5pkXoCZnweK6XpebM2OUfKDQ2K8VnWekZqlfaQi81ZL/FpoV8KubBVMrMiUvPpMrtfGWPZOY3YnVYSd94FFgvBj3wEgG1T23hp9CU+kkzQ3X4d/ckiN5zbcdyvO99YFvfyQiZCg6riFG30eIzvGZe1VBezUsf7sgRiLtTtmxFdLuwLF7JxbCMrIiuwWebOVn9qw6k4rU6eH35+Zl6qZ3grgsC8sfwOd6WQ7BaibR7UXI7Cxo14zj8fMKqp0TYv6DA1mKvxSucHuq7zp8RmzlIt3OBo5+XKJJuHX6r1skze5Jgi9RgQJInQ9ddTWL+e4pYtADQvDRJqcrPl6UF0Xa/xCuuflx7eC8DO50dmtSfrRDDclUJT9X0i9dHHsDY14li1Ck3XeGjPQ5zddDbxridAKcGav4W3/gOMbMLS8zTXLb+O1yZem7FtSU1NeM47j9TvfocuGxfWC1c3MNGfJT1R+7v59UBVpNomepDaWhnMDdFeSDMmGcm+9Wb3bQ+7GdSjRHWFgtuCvZyiXFCQK2/srChOCytLZoIpl0aDqjKih+uykgoCeXuMBsFKwWX8fOqrkmqsxaikCgR1iQyuuhH8rSEXw3qYsKYjIpD1SdgKiVn5/tu6dghN01l1QQtqOk3q/vvxv+sypJjh0rhty214EHm/Yuc7w6to9Du45OT5E4AzWyyNe9lQbEQEOiQ/25zG6BaXnCaXKqOpWk3XNzGQJTrdj+pYsYKiVmZHYsecWX2r2Cw2zoyfyfPDz8/MS5U3vEy4xcPoPBGpQ7tTNC70I1pECi++CIpC6IaPIbpcFDdtomE64XfctPweEduHX2QIhUsip3HNW76ET1W57cVv13pZJm9yTJF6jATedw2ix8PUbbcB+8bRTPRnGd2bqfHq6puhriSDO5Oc/LZmVFVj85MDtV7SG9K/PYHVJtK4MICayZB7/nl8l1yKIAi8PPoyw/lhrljwXlh/K7SeZcwdXPUB8DXDuh9y5aIr8dq83LX9rpljBj5wLerEJNknDRvwwtOigGn5rTIzI7V/J0pjBEVXaC8V2K0345BEWussWKgz7KZfi9GkqCS8Aras8XM8XF9qtRJpSYww6VKJqiplVyMOyXLC13w0+J0SHruVKUuUmKqTdOVA12ZEdj1grEVHkvOk3eCyhgChbiqpbSEXMlY0ZwNRQSLpFbBmxynlZNTjEEeqrLFt7RDtK8IEYi6S992HXigQuuEGAPoz/Tze9zjvTyUpL7uOZ/Zmue7s9pqP5akFS+NeJvEh2wJ06iJ7SgNYo1Hs2VF0TSd3BH3kJ4pirkIuUSbc7KK8YwfOVSvZMrkFVVdZHT0V1n7PCOV79kfG35rNv4GuP0Hf8zC6FZJ9UEiAOjv21XOazqE/289gbvD/sXfe4VHdZ77/nOl9NKPRaCSNGiCJJtFMbzZ2bMCJS+xgxw1jp22cZLPJJpvdzW7u3iR7N5u7ieMk90kzGNux4xZc4oIx2AZMR0IFAZJQ79Joep8z5/5xJHB3TIw0gD7Po+dBozPid0ZTzvt7v+/3e3YutdTKQFtgwov5jyIaTODtC5NffnYeVWE0Ypg/H11VFdFjtRisGvQWDcOTRepfxfbazagkiTVzNmEsXc3n0wZ2Blto9Wa+Gm6SC5dJd99zRGkyYbv1Fjybt5Do7kbjdlOdhMCVAAAgAElEQVS+KJf9205T/3oXeVOtE73EjESSJA690IbBqmHFzdOIBhM07O5h/tpiNLrMfDp2NnooKLehVCvw/WUXvE3qu615G2aNmTVpNYychtX/JN9JpYFlX4dXvoehr44N5RvYcnwLXcEuCs2FmFatQpWXh++JJ7BcczUWhx5nsZnT1YPMv6Z4As82M/APxzBaNYjdHUSWlgFQkkzycjSHaU5TxsSejJGfpaNP4WRFKsWASaR8pAey5azUrNwPLqjHOpFKTw/+QqhISRhsmWftP5aV2pNykJvoZUCnIysYIjySOWZfsVASrSqNgETAAFLQgVmrwqLLjLzP4mx5NtSnzSMvHWTAkKQsLBuyxYLJc87obD4yQDSYZM4VhUiJBN5HHsWwdAm66bKD60PHH0KJxB2hBL8JX45OHeXzC4s+mZO6wBhz+B3Wl1ISi/ByIoSyaDaa4Q4wzCQwHMXimJhNjeFOWXaaJY2QSCbRVVVRPVCNgMCcRBJ2/RAQgL9CqaU2gNYCWrP8pRv7t+Xs7bq3/VxrfeexOivL8pcBsL93P1eP5qU6zDEa4iKenvCZrNFMZGweNb/MhiRJhPbsxrB0CYJGg37uHDy//wNSNIqzyMxQ12SR+lFIksSrQ0dZkpR48JQTS1cbt1V9ga3Hf8nmgz/hR2t/N9FLnOQiJTOrggsE25134nloKyNbH8b1r/+CRqdixvI86nd1E/bFMyIYPNPoPumlt9nHylvKUWmUzL+6mNaaIY7v6WXepzLvwikwHMU/GKXycrf8/Ssvo87PR1dZSSARYGfnTm6YdgPao1vBkA0zrz975/l3wZv/DXt+xm03/oqtjVt5tPFR/nnxPyMolWR97maGH/gliY4ONMXFTJ3vZP+20wQ80QsqMP184B+MYDIrIJViwCZ3fIqTKfb67FSUZd7FkUqpQJNVQG5KotcMM3raIPujs1LHYlM0sQB+g4AmbaTAlpl5uW6bgfZeG7mxMO1aFU5PkIgnc+a5oqEEGhKkNEpMahjKsNlep1mLRqVgUJGLK+GhWxdl1mgmZSSQOKfPC0mSqN3VhS3PiHuGjcDzz5MaHCTvRz8EYDg6zHMtz3FdKIJl+o08UhPls/MLsBkzIzZovLEbNeSYtbQpCin170OyGYi77KhrTkL5ugl1+B0rloyDTSQAfVUV1XVPUW4rx9x1BBDgu62g0kE8APEgxAJn//1RtwUH3nnbRxS7JZ/9A3nGPPb17uOGRd8GwDJ0AnDSd9qf0UVqT7MPlVqBs9hMorWVVG8fpi99GUB25BdFYsePk1OUTWfjCKmEiEqTWeqVTKKxv5oeknwhazb/srsdlVJgw3du47NHfs6TA/v5Wrgfl/HSGx+Y5Pxz6el9PkHUublYr70W39NPI/rknbvK1W7SkkTDZBzNe5C7qK2YbFpmrsgDILfUQkFFFrU7uxBTmSch6myUZ5aKZtoR/X7C+/ZjXitLfV9pe4W4GOdG13I49ZJsmKR+m1OmxghLvgrN23EGBlhfup5tLdvwx+WZnqybbgalEt+ogdJZl99L20AplRQZ7AhiN8kFXIc5jkVQYVUZOR40ZJxp0hhFORb0aRMjFgGNR379f5R5UjSYQKkSUKYT+IwgJs2j85+ZR6Fdz6molVwxxbBBRJMMEfVPvNnMGNFgEk06StisJieZpDNlyxipL8jusoU2PV1iNq5YkA5NAE1CHg0J+89NZtrX4me4K8ScNfImmuehrWimTsW4YgUAfzzxR5LpBHd7vfxZez3xVJq7l5V+Mid0gTLdZaYuftbh15ujRdXXiqCY2KzUwY4gFocOsfEYKqcTwemgdqiWec550L4HXJVgsIPGAGYXOMrAvQCmXgEzr4N5d8CSv4PL/wmu+TFc9wB87iG44xn4wg647wB86zj8cxf8+wh8rwv+oRG+ehDu3SEfd/MW+MwDYM5HaHiaZfnLONh3EPJzUblcUHsQY5aW/tO+CXuc/hp6m3y4plpRqhSERl19TSvl18RYbNyYeZKUlhjuyZzNtkxke70s9bVaPk1CTBNJiPy5zsPGkk+DJLG15tcTvcRJLlImi9S/Efs99yBFo3j/9AQA1hw9JbOzOb6nBzGZeUXXRNJ5fIT+1gAL1pWgetvM3fyriwn74jQd6p/A1b0/XY0jmOxasnINBHe+U+r7bMuzlNnKmNm6DyQJLpNnwHyRBMmxmZ1FXwCNCfb+nLtm3kU0FeWpJrkoVec6Ma+5At8zfyadSGDN0ZNTZL7k51IHWgOIyTQ5ymEAThn8lEhKIpYpgJBxpkljFGcbSCZsDJtBJcZRa4WP7KRGQ0l0ow00vxGiiczq/r0dt81Aa8pObkrEaxJQJ4JEw5kT3xANJVEng/iNkCOKnI5bMiYjdYzibCNNCTuuVJIBQ/JMkXquBlR1u7rQGlWUL3YROXiQ+IkT2O/eiKBQEEqEeOLkn7gqnqaoaBW/qFOzfFo2Fa7M3OQZLypyzewPOihOpRAQ6M2SUEhpjCblhDr8yqZJZmJ19eiqKjnlPUU0FWWBoxK6DkHpqk/uP1MoZFmvtQCc06FwEUy7CmZ/FhZshBmfgdY3WOZcQCgZ4rjnOIZFC4keOYxrqjWjHX5j4SSe3tCZfNTwnr1opk5FXVAAgMpuR11cROTYsTPd4KGOScnvByFJEtv7D7I0nuL54QocJi1z3FYe3t+Ba8nXWR+K8EzrC/himb1xMcmFyWSR+jeiqyjHuGIFI48+SjohX2hUXuEmGkzScokXG29nrItqztYxY1neO35WONOOo9BEzaudSOnMcUZOi2m6T45QNDMbQRBkqW9BAbrZs2nxtlA/XM8NUz6DUL0Vyj4FthJC8RRr/udN/tfzx+VforfBwnvh+DYqJBVL85by2InHSIpylzBrwy2IXi/BHTsAmDo/h4G2AMGRzOlQjTfdp7wIAthC7Qg6HcfppTgWZVAry8HLnJl5kV3qMBKMOxmxyN8bdH9NJzWJViU7AIcM4BcdGefsO4bbpqd3NCvVawJNMkgsg56msVACdczPiF4kJ5WiNW6lICuzDLaK7AaOh624xh7D0SL1XBx+A8NRWo8NMWtFPmqNEs+WLSizs7Fedx0ATzc9TTAZ4p7hQQ65bqU/EGPTJd5FBdk8qTFZgE6SyFebaDGFATBqkgQ9E9NJjUeSBIaiZDvVJNrb0VfK86gAc1MSiHEoWTl+C6pYC6kYi+MJFIKCfb37MC5ahOjxkGNNEvLGM/YzqrfZBxIUlGeRjkaJHD6MaVRZMIZ+zhyitbUYszTojOrJudQPoWGwll4pzlWWCnadGuFTM3O5e3kJrcNh3vIY2WSbQ1QSebzxkYle6iQXIZNF6idA9j2bEIeHCTz/PACFM+zYXIaMd60dT9rrhhnsCHLZ+hKUqnc+7QRBYN7VRXj7I7TVDU/QCt/LQFuAREykcIYd0ecjvG8/lnWy1PfZlmdRCSo+nVJDaAAWfgGAPx3qZCSc4E+Hu+jwyBc/LLkPFGp46xdsnLWRoegQL7W9BIBx+TLUbje+0U78mOS3tebSlfz2nPKSU2xB6m5D5S6gPzZIcTRAq1SAUaPM2CKuONtIXzoXcTQrVa9KfmSRGgsl0AryMRptmgEpO2M7qYU2A/2SnVxRJKwDtRgmKSoyQjEipSVi4RSq0AhD+hROUaRfys64Tmqh3UBzwoYrJRLXCAh6FSohRcT/8YvU+jd7QBCYvdpN/PRpwm/uxnbb51FotSTEBI80PsIiUcls6xT+u7mA4mwDa6Y7z8NZXVhMd1kYwkpCbaUEDce18meOQQpNWCd1uEuWm1qS8vu+fk4VNYM1FJgKcPXWg6CE4mXjt6DiFaAxYz39JrMds9nXczYvNcvXApCxUTS9zT6UKgXOEguRw4eREgmMK+UCv8nbRCARQD9nDuLQMGJfHznFZoYmHX4/kFcbtqKSJPJs1xJOiFwzK5f1lXlkGzVs3dfBtCVf5/JwhD82PkIkmTlGepNcHEwWqZ8AhqVL0c6YgWfLQ0jp9Jk4msGOIP1tmflGPp5IaYmDL7RhzdEzfcn7D9dPm+/E4tBRvb0jY3JmOxtHEARwT7cR3LkTUinM16wlmU7yQusLrC5cjf3Yn8BaBNOuIimm2by3jVn5FtRKgQd2yh/mmHPleaFjj7HMVMK0rGlsbdyKJEkICgVZGzYQOXyYeGsrWU4DjkITLUcvzS58IpZioC2Au8JGsrODZF42IJsm1cVymZZrzjhn3zFKs410SU50erkzqpMiHxlpEQ0m0YhRRKWAUZ2ZGaljuO164mjQK6woBQWCSj63sXzSiSQeSSGlJVTBYfxGcIgifZI94x7LYruBPikbV0qWScftRrRSjMjHlPsm4yIn3uplytwczHYdIw9tRdBqsX3+8wC82Poig9FB7hnqo6v8bqq7/GxcWpKxr53xpCzXhEIQGNSVUBqPcyrRhTI7G310mEggQeojso3PB2OdPEN/IwgC2lmzqB6oludR2/ZA/lxZnjteqDQwbQ00bWdZ3lIaPA1EnBZULhfqxv2otMqMlfz2NvvILbWgUisJ7dmLoNNhWHgZwUSQ2168jV/X/Br93LkAsuS30MxITzgjNtsyDUmS2N67l2WxBH8JVGHWqlg21YFWpeTWRYXsPDlAV9Zi7sWKX4zyTPMzE73kSS4yJovUTwBBEMi+ZxOJ06cJ7d4NQMUSF2qdkvrXuyd4dRPP6ZohPN0hFn66FMVoNl+yp4fWG24k8PLLACiUCuZeVcRAW4C+lsyYbeg6MUJuqQWdUU3gle2o3W50s2ext3svI7ERbshZKBtaXLYJFEperOuj1x/jW58q584lxWyr6aZ1aNSQYfk3QEojHPh/bJy1kWZvM/t79wOQ9dkbQa3G98RoN3Wek/5W/4Rm9k0Ufaf9pNMSBeVWEp1dBJyy021JMslbfjvlzsycRwU5hqZX4SRHEAkbBLRxH5FAgvSHSNijoQTqZJCQUcCZFglqnJgzJDLl3Vh0aqx6NX6VkxyUpDSjRWoGZKWOFcrqRBC/UcAhCoxgzriudFG2gTgaDJpsNAiErVo0yeDH7qSeOtBHPJJizho3KY8H/3PPYb3+elR2O2kpzeaGzUxHxzLByAND8zBpVXzuMvd5OqsLC51aSUm2kVbclAaHiaai4Hah8cqf1RPh8DvYEcRk00LjMTRTp9ArefHEPMzPng09R8dX6jtG+ToI9bNck0NaSnOw/yCGRQuJHT5EbomZvgw0T4pHUwx3Bc/ko4b37MGwaCEKrZbd3buJi3EO9R9CV16OoNOdMU9KpyU8vZPmSe+mfqiOvnSMq42lvHQywJoZTjSjSrjbFxcjAI8e6mTugq9wWTTG1ro/nBllmmSST4LJIvUTwrJ2LSqXi5HNWwDkOJqlebQcHTxn58aLgXRa4tBf2rC5DJQtlPMf09EoXV//OvGTJ+n/8X8ihmRZ7PRleejNaqq3d07kkgHZfGGwPUDhDDspr5fw/rNS320t23DoHazoqAGlBubdiSRJ/HZ3K9OcJq6ocPLl1VPRqpT8Ymez/AttJVB5MxzZwvrcJTj0DrY2bgVA5XBgvupKfM8+RzoWY9qCUZffmkuvm9pz0otCKZBjSSLF4wzY5M5PoQi1YVvGOvuCHEMjWYvJT6UYNoEmPISUloh+wLxhMiGSSqRRRX14DRI5KRHBWjDOq/54uG16+gUHTlEkppVftx+3C3g+iI5F+SRD+IxgUFhRKhQ4zRkm97XJM7I+tYtclHjNApqY92PNpEppibrXu8kpMuOaasX72ONIiQT2uzcC8HrX67QH2tk02E2k6m6ePT7CzQvcGbv5MRFUuMwci7sojcjdwEiuFXX/aWBiHH6Hu4I4Cs1E6+vRV1ZxdOAoAPNTQDoJpRNQpJZdDYKC2f1NmNVm9vfuPzuXapfwdIdIxDLHOA2gr8WHJEFBWRaJri4S7e2YVsiP3c7OnQC0+Frwi2F0s2edKVKBScnv+/Bq4x9RSRIljvV4I0nWzjqrhMvP0nP1TBdPHO4iNmsD94aTDMRHeLHtxQlc8SQXG5NF6jkwEB7gRwd+dCZKBEBQq7HfdReRQ4eI1jcAUHm5m7Qo0bi3d6KWOuG0HB3A2xeWu6gKAUmS6Pv+vxE/cRLH17+GODyM58E/AKDWKKm6wk1HgwfPBFvCd50YQZKgaFY2oTGp79q1eKIe9nTv4TPF16CqewJm3gCmHPa2DHOiL8AXV8rn6TBp2bishOdre2keGP3wW/EPkAyjObKF26bfxr7efTR5mwCw3XIrab+fwCuvkJVrILvAeEm6/Haf8uKaYiXdJ89zd5jjOFGjMBWRQkV5hjuT2h0uHCmBYQsoR+TOzAc5/I45uqqCw4wYIFtMY7RndtZcoc1AR8pObjyGTyu//52rM+0nSWy0m6tOhvAbBQTsuCw6lBkmb9VrlDjNWvoFJ65kiiGjiDo4/IEbGe9H14kRvP0R5qxxI8XjeB97DNPll6OdMgVJktjcsJkChZ6rYykeSV1FKi1x97KS83dSFyAVLjOHQk5Kk/LzxpOtRtMjbygGx7mTmoil8A5EsGdJiB7PmXlUq9ZK6UATKFRQuGRc1wSAMRvci1A1b2dx3mL29e5Dv3AhAFmhdiQJBtoD47+uD6G32YdCKZA7xUp4714AjCtXEEvF2Nuzl3JbOQBHB4+inzOHeOMJTBYFWoNqskh9F5Iksb37TZZHY2yPLECrUrC6Iucdx9y1rBhfJMnzJ4Msn34zFYkkm+t+T1qalE5P8skwWaSeA764jydPPcmWhi3vuD1rw+dQmEyMbNksf59roGhWNg1v9mRkBuj5Ji2mOfyXduz5RqaNGgKNbN5C4MUXyfnmN8m57z4s69czsuUhkv1y/Mzs1W5UWiXVr3ZM5NLpOjGC1qDCWWwm8PIrqIuK0M2cyV9a/0JKSnFDSi2Hoi+8F4Df7W4lx6zlhnlnO2FfXjUFg1rJ/WPdVOcMqLgWDv6GDSXr0av0PHz8YQAMixehKSnB98STgGyg1Hfaf0l14WPhJENdQQoqbCQ75W76SYOPkpTIkLYYIGPjZ8YodhhRJs14zKAalJ/D4Q+QbcdGu38K3wB+I2hTegrsmX1+bpueppiV3GScAY1sOBPxTly25Bhn5L7JEEEDRFOZa0BVZDfQIWbjikfo0UVRx/3EoylSyb9uFrJ2VzcGi4ZpC3LxP/88oteL/e67ATg6cJS6oTo2egZh1s38vibMmgonJQ7jeTyjC4/pLjNNaTfZYhqzQkO3NYUmEUCpHH+5r6c7BBJY4vJnoK6ykprBGublzEPRvgcKLgOtiUQqTSwpfuj4wCdOxTror2epbQZ94T66rSlULheG5gMgZJ55Um+zj9wSC2qNPI+qdrvRlJRwoO8A0VSUr8/7OlqllqMDR9HPnYuUTBJvbMRROGme9G7qhuvoFyNcrSvg2aY4q8pzMGhU7zhm6ZRsypwmHt7fDou+yL0+P23BTl7vfH1C1jzJxYfqow+Z5N1U2CtYP2U9fzzxR26fcTs5Bnl3SWkykXXLBka2PETOt3rQuAuousLNX35Vy+maQcoXZnaX5JOm6fAAvoEIa788G0EhENqzl8H/+R/Ma9eS/aUvApDzrW8R3LGDoft/Qf5//R90RjWzVuRT93o3iz8zBYtj/C80JUmiq3EEd4WNdMBP+MABsu+5B5CzUascVUyp3wa5s6FwMY29AfY0D/OdayrQqs7mv9qMGu5ZUcovd7Xw9TUBprsssPJbcOpFrA1/5oZpN/BU01P8/fy/J8eQQ9YttzD4k58QO9XE1PkFHHqhjdaaISovvzRmycaiA9wVNhIvdIFaTb2ij6uiQdoUBZi1KlyWzJJvvptSh5FUwobH7EHXICsoPriTKhepKt8AficYUsaMM/p5N26bngOpbJamRKr1YbIjScKDE3+hOvZYahIhFDqR7nBWxj6WRdkGmjw2XJokddoQy89kpSYx25Ufel9vf5jO4x4WfaYUhRJGHtqKduYMDItl59XNDZuxKbTc4O9iZ9bNeMIxNi2fjJ15NxUuC4NkkVSZKVXoaDIGWYSE0SARHGe575hpkrHnODGtlnChg/Yj7Xy2dD3s/SOs/BaDgRhX/N83CI+aOqkUAlqVAs3bv5QKNColGpUC7eiXfNvbf64Y/fnZ4959jFatwKBRsmyqA13FOnjtBywLy2s80HeAyxctJPzWPuxrb84o86RELMVgR5D5VxchJRKEDxzAev11CILAzs6dmNVmlucvpyqniiP9R9AvuhtgVPK7grrXuxDFNErlZO8GYPvJJ1FLEjNy19J7Ksa3rq54zzGCIHDXshL+7dkGaqKz+ZRzEYXiaR6s/wNritYgCJmlZJnkwmPy1XiO3DfnPlLpFL+t++07brffeScoFIxslecNi2basTr1l5yBkiimOfyXNhyFJqbMzSHR0UHPt7+NtqyM/P/88Zk3L427ANtdd+J/7jlijY0AzLmyEAE4tnNiIny8fbIra+FMO8HXXgNRxLJuLcc9x2nxtXBD9lzor5e7qILA7/e0YtAouWNx8Xt+1xdWTMGsVfHzHbKsF/dlcij7vl9xZ9kGxLTIYycfA8B6w/UIGg2+J57AnmfEnm+8pFx+u095UakV5JZaSHR2oszPw5sKUJxIUB/PpSzXlPEfeiXZRiLJXDwWuaunUHxwVupY908T9eM3CqRSWbgztPs3RqHdIGeliiJes2xUFBmLWppAoqEEKkEkrRQxK0VOx60ZFz8zRpHdQGNUzkr1GKWzWal/hXlS3evdKFQCs1YWENq9m0RrK9mbNiEIAk3eJvb07OG2YARd6Wp+Ua+hPNfE8mnZ5/uULjiK7Ab0ahX92hJKkilqtfL7rFERG/dO6lBHEL1Fg3SiGt3MmRzz1gMwT1SAJELJSt44NUQ4IfKV1VP55lVlfGnVFG5dVMSnq/K5vNzJZcV2prssFGTpserVKAQIxVP0B2K0DIY41uVjb8swrzT08/SRbjbvbeOBnc38dPspfvzSCX7w/HH++c/1fPupWr72WA33PHSEB/e2gaMcbKW42/ZTbCnmrZ63zsylOh0C/a3+8e3sfgj9rX6ktER+eRaR6hqkSATTypWk0ine6HqDVYWrUCvVLMhdwCnvKWI2Par8PKK1tTiLzKRTEiO9E/9elgmkpTQ7OneyPBJlZ2IxSoXAVTPeP77qs/PkDeSH97WjWvxl7vZ6qfc0cLj/8DivepKLkclO6jlSaCnkpvKbeKbpGTbO3EihpRAAtcuF9dr1+J55hpz7vooyK4vK1W72PtXMYEcAZ/E42shPIKf29xMYjnHtV6tIhyN0f+1rCIKA+9e/QmEwvONYx5e/jP+ZPzPwk/+m6KEtmO06yhflcmJvLwuvLUFv0ozr2rtOjABQONOOb8srqIuL0M6YwbYDP0Kn1LG25yRozFC5gV5flBdqe7lzaTFWw3uNSawGNfeuLOX+15pp6PEzu8AKK78ND19PYeturiy6kidPPckXK7+IwWbDfM01+J9/Huc/fpup83I4/FI7kUACg2V8H4OJoOeUl7yyLJQqBfGTJ0kUOIBeSpJJfhfLpqIys+dRQS5SXxPziJiOIyCh1wuEfO9/0Xu2+xfEZ4RwMge3zfC+x2YKbpscoeJMifhMAppkkGjANtHLIhZKohXihExKnGKcnrSN1VmZ+VgW2Q28kM7BlUoxYuZskRr4cGl/PJLk5IF+yi/LxWDR0LHlIVS5uVjWrgVgS8MW9Ao1nx/u5lTlD2lsDPCfN1Zm/MbORKBUCJTnmmiJuCkN1fC8OYLCakWX8NI7PL7vM0NdQXLcRuKvHsd2ywaqB6vRKrXMHGqTjfkKF/Hm/hPkWrT809qKT+zvKUkSCTFNIjX69bZ//+PTdbxQ28t9V0yTJb+HH2TplffxXNuLqJd9GwBbvIdkzMpIbwiHe+Lfm3ubfAgKAdcUK75f7QG1GsOixRwdqMYX93Fl0ZUALMhdQFpKc2zwGFPmziVSc4yc7501T8opnPhzmWjqhuroT4X5hjqb+08rWDLFTpbh/a9BjFoVNy1w88eDHfzrusu5Xmnn/0kKHmx4kEV5i8Z55ZNcbEx2Uv8Gvlz1ZVQKFb869qt33G6/5x6kSATv6Hzh9GV5qLVK6i6RbqqYSnP4pTacJRaKZtno++fvEW9to+D+n6Nxv1e6qrRYcHzta0QOHiT0+hsAzLu6mFQyPSEd6M5GD1m5BgyKGOGDB7GsXUdcjPNy28tcVbASc+PzMOdW0JrY8lYbEnDvig+W1N2zohSrXs39r412U0tXQ/58eOsXbJxxO4FEgGdbngXAdustpEMhAi+9xNQFTpCg9RJw+Y0EEoz0hmWpb3c3ifZ2PLPyATkjtTaaQ5kz8y8e8rN09AlOVKNZqQZ16gM7qbFQAoUClGKMiF7CKzoyVqI6htumZxgLjrSA1yTLa6OhiXf4jIaSqFMRfAYJhyjSL9kztpNanG2gR3LgSon4jaBOylLKj3L4bXyrj1RcpGpNIbHGRiIHD2K/8w4EtZreUC8vt73MTQkFVvs0HmgvwqpXc+O8zHaLnkgqXGaqo7lnHH7Fghx0wX7ikRTx6Pg8p1MJkZG+CDZjEikWQ1dVRc1ADbMds9F07AP3IlIKLXuah1hdnvOJbjgIgoBWpcSsU5Nt0pJn1VOcbaQs18z1c/I52R/k9FAIyteCGGc5BqKpKA3aIVQuF8Y2uVPW15IZkt/eZh/OYjManYrQnr0Y5s9HaTKys3MnWqWW5XnL4C/fYk7Ai0pQcWTgCPo5c0j19WFIB1DrlJNzqaNsb34WTVpijutqTg+FuWbWh4+q3bm0mKQo8acjPWgXfoE7vR729e7juOf4OK14kouVySL1byDHkMPtM27n5baXOTVy6sztuooKjMuXM/LoI6QTCbR6FdOXuGg+MvCxogYuVE681UtoJKAuoYUAACAASURBVM7iz5Ti+e1vCe54jdzvfgfj0qUfeB/bLRvQlJYy+NOfIiWT2PONlFQ5qHujm2R8/MLVU0mR3iYfRTPtBHfskKW+a69hV+cugskgN6ZUICZg4b0EYkkeP9TFtZV5H9oBs+jUfGnVFF47McixLh8IgtxN9bYzd7CNOTlzeKTxEcS0iH7+fDTTpuL9kyz5tbkMtFQPjdv5TxQ9p7wAuKfbzmQNN083owRy1NmEMGR0/MwYKqWCpKUY42iRqiNC2Pf+r/loMIlWIyEASl0ar9JJ1vt04zMJo1aFzahDI2TjN8iS5lh84uV+0WACdSKIx5DGKYr0SdkZW/AX2g3E0JIlmBCVAsLoc+XDPhvSaYn6N7rJm2Ylp8iM56GHUBgMZG3YAMDDjQ8jILGxtxVv1Rd4pXGQWxcVotd8+IzrpUyFy8KxeB4low6/IacZzVA7MH4xNJ6eMFJawhIdTQCYUcaJkRPMt8+EvjooXcWxLh+BWIrV5e8vtzwfrKuUi5KX6vqgeBlorSwcbEElqNjXtx/DooVwZDcGqyYj5lKTCZGB9gD5ZVkkBwaJnzqFaeUKJEliV9culuYvxTDcBEceRH/0IWY5ZsnmSXPmABCrryNn0jwJGJX6tm9nWTTKPpYDcPXMDy9Sp+aYWFnm4I8HO0nOuYMNkRQmQcnm+s3jseRJLmImi9RzYaQVnrwLQoNsmr0Jk8bEAzUPvOMQ+z2bEIeGCbzwAgCVV7hJpy7+OJpUUuTIyx3kTbWS1V/L8AO/xHr9ddjuuutD7yeo1Ti/848k2trwPil3oOdfU0w8nKLxrfF7zPpa/KSSaXke9ZVX0BQXo50+nW0t2ygw5nPZ8ZeheDk4Z/D4wU5C8RRfWjXlI3/vxmUl2Azqs7OpFeshZzrs/RkbZ9xFd6ibXV27EAQB2y23EmtoIHa8kanznfQ2fbwcxQuR7pMjaPQqHIVmwrv3oC4qotHowy0p8etLgMx39h1D6yglR0gR0oM2GSDkiyNJ7y3koqEkWqXcsdHqRNKmvAtCmllo0zMiOLALSgQhQiypfN/zG09ioSSqiBefEbJFiWGs5GdokZpj0qJXK0mocjEgELOqUUvxD32Nt9cOE/TEmLOmkGR/P4GXXsZ6800oLRZ8MR9/bv4z6wULLo2VPwQWyYYmS0vG76QuQKa7zDSnCyhMplAiMGRXou5rASA4PD5zqUOdstTb0F2P0mbjhM6DKInMF5WABKUrebNpCKVCYEWZY1zWBJBn1bOg2MaL9X2gVMO0KzE272Kuc+6ZvNS0x0NuriojHH4HWv2kRYn8sqy3Rc+spNHTSH+4X5b61j0lH9y+l8uc8zk+fJx0WQmCWk30mJyX6ukOkRYvvSSGt1M3VMdAKsQ1GHis3cTcwixc1o9WpWxcWkJ/IMaOtgTm2Tdzqz/Ijo4ddAQmNqlhkgubySL1XEiLcPJFeP3HWLVW7pl9D7u7d1MzWHPmEOOyZWinT8ezeQtSOo3NZaRwho2G3T2IF/Gb4PE9vYR9ceYt0NL33e+imz0b13/8x1918W264goMixYx/KtfIwaD5E21kjfNyrHXOsftMetsHEGhEsjNkQgfOIh53Vr6wn0c7DvI9fZKFL5OWHgviVSaLW+1s2xqtjxn+hGYtCq+snoqbzYNcbRjBBQKOTd1sJE18SRuk5utx2WzLev11yHodPieeIKp851IErTVXtzd1O5TXgrKsyApuzKaVq6kI9BBcTxGh1BAlkFNjlk70cv8q8jPsWFJqhk2gyo0SCoukngf+WA0mEAjxRAFMKlFVLYLw8XZbTPQKcrmSWllhDTKcVU7vBtJkoiGkqjCI/iNYMSIxaDFqM1MywVBECiyG+jHiUsEv1mBRgx/aFZq7a4uzHYdpXMceB99FNJp7KMbf4+ffJxoKsqmzhMk52/ikSODXDMrN2M7yZlChcvMADYklQm3Qk+nJYk+Ir/PBjzj00kd6gyiNaoQjh9GV1VJzeAxBATmjPSASg8FC3izaYh5hVlY9eOrsri2Mo+T/UFah0LyXGp4kGWmYk6MnCBeVQaATRwgOBIj9AExW+NFT7MPQYD8aVmE9uxBlZODtrycnZ07UQpKLi9YCQ3PgC4LEiEWKC2kpBT1gZPoZs4cdfg1k0qm8fZHJvRcJprtLc+jkSQuK7iKup7AO6S+kiSx86FGXvldw3v+5ldMd+K26dm6rx0WfYnbvSOoBcV7ohonmeTjMFmknguOMlj4Bah+GAaOc/uM23HoHdx/9P4zHQVBEMi+ZxOJ06cJ79kDQNUVhYR9cVprLs6CI5kQOfpKB/lTzKR/8o8IOh3uXz6AQvf+u3ChROgdHRhBEHD+03cRfT48v5Vdk+dfXUxoJE7L4YFxOYeuRg95U7OIvbEL0mks69bx3OnnALi+7zQYnTD9Mzxf20t/IPZXdVHHuHNpMQ6Thp+NdVNn3wRZRSj3/pw7ZtxB7VAtxwaPobRYsKxfj//FF8mySmTlGi5ql9/AcJTAcIyCChuRw0eQYjEMq1bSGeigOB7leCKXcqf5gugyghxDI2elCii9PcD7x9BEQ0k0YoSgARzpNCZH/ngv9Zxw2/S0xLNwJmLEVSFALrgnimRcREym0SSC+A0CKsma8QVaod1AWyqbvESMIWMaTcz3gZ3Uoa4gvc0+OYoqFsX7xJOYP/UpNG43kWSEx04+xmp1DtPSAs9priUQS03GzvwVOExaHCYtfepiSkWJk0YfqlQEtRoC49VJ7QrhyDeQaGlBX1lF9WA15bZyzB0HoGgxwzGo6/azujxnXNbzds5Ifuv7YNpVIChZFpS7poeVnahcLkzdxwDZWXci6W3y4Sg0o1ZDeN8+jCtXnomeuSz3MrL66iHUD1f+OyAwz9ePQlCM5qXOIdbQgCNffs8YiwS6FElLaV5tf4UVkSjVyhUAXDMr98zPW44OcvJAP601gzz+vw/S+FbvmWs4pULgziXFHGwb4aRQgsO9mBtjIs+ffp7ByMV7/TLJ+WWySD1XVv8TaC2w/V/QK3V8peorVA9Ws6dnz5lDLOvWocrNxfOgrMsvmp2NxaGj/o2L00Cp4c0eooEEJS3Pkujpwf3AL1Dn5b3vsc+2PMvKJ1a+RyatnzUL63XXMbL1YRLdPRTPzsaeb6T61c7zLikM++J4esIUzbQT2P4KmpIS1GXTeK7lORY5qshvfh0WbERSqvn97lYqcs0f6+LBoJG7qW+1eDjQ6pFlVMu+Ad2HuVHjxKKx8HDjw4A8oytFIgRffJGp83LoafKdiSy52OhpGp1HrbAR3rMbQaslPLuEmBinJJniQNBB2QUi9QXZ4TeVtDFiAfWwHKP0fuZJsWACVdyHzwi6lJZ8W+bP3AK47Qa60tnkplIE1T7grFPxRBALyf+3OhnCZ4RE0paxUt8xiuwGTsaycKWS9OqjqMMjhP3v342q29WFSqNgxvI8fM/8mXQwSPamuwHY1rINX9zHPT3NSLNv4jdHQ8wusHBZ8cQ7Ll8IVLjMnEq7KYkEqFUPIgBGdWJcOqliKo2nJ4RNGwVJQlM5i9qhWnkedfA4lKxkb/MwAKsrxr9IPSv57QeDHYqWMKPtAFnaLPb1ynOp6iM7UakV9J32jfv6xkglRQbaAuSXZxGtqycdCGBatZJWfyut/lbWFK2B+qdkR/65t0FeFab2fUy3T5fzUufORYrH0Xnl19lQx6VbpNYO1TKYDHJ1SsnD3bmU55qYkiN/9sajKfY+1UxOkZnP/2AxDreJ1x85yQsPHDszw73hskK0KgUP7++ARV9k42APYjrFI42PTORpTXIBM1mknisGO1z+PWh9A5q289myz+I2uXmg+gHSkixNFdRq7HfdReTQIaL1DSgUApWXu+lr8V90u3WJWIrq7R3kGoLodv8Z1/f/FcOCBe85Li2leaD6Af7trX/DqDbyYP2DHBs89o5jcv7hm6BUMvSznyEoBOZdXcRIb5iOBs95PYex6Jn8fCWRg4cwr1tL9WA1PaEebhS1suHRgrt5s2mIUwNBvrhqysfu7t2xpBinWcvPdjTJRfe8O8DoxLDv12yo2MDOzp10BbrQVVWhnTED75+eYOr8HKS0RNux4fNx2hNO9ykverMae76R0Ju7MSxaREeiD4DiZJL6WO4FYZo0xpmsVLOAfkQ+j3cXqWIyTSImogh78BsElCkjBRmekTqG26anV8rGlRLxauSL08gHxOyMB2eifJIhggYYidkyvpNanC13UnNTKXr0MbSJANH3iaCJBBI0HR5g+pI8tDoFIw8/jH7ePPRz55JMJ3n4+MPM1TmZHw5QnX8bLYMhNi0rvWBUBxNNRa6F6qiT0kgAjy4BRgP6dHBcOqkjvWHSooQlIm9ad7t1RFNR5kujMvXS1bzZNITdqGF2/kePlJwP1lfmcaIvQNtwGCrWoRhsZKmjin29+zAsXITkGcLhUk/oXOpgewAxlaagLIvw3j2gUGBcupRdnbsAWJO/HBqfhxmfAbVedtfvOsQCRxV1Q3UoZ88EIF5Xi8NtvuiuzT4O21tfQiNJLMm/nEMdfta+Tep78PlWIoEEl99egc1l5IZ/mMfq2yrobw3w+A8PUf9GN1l6NdfNyWdbdQ/+knUU6nJYK5h48tST+OMTP7s8yYXHZJH6t7DwC5A9DV79Pmrgvnn3ccp7iu3t288cknXLBhQmEyNbZF3+jGV5qDSKCYlWOZ/Uv9FNLJTEvec3ZH3uc2Tdcst7jomlYnznze/w+/rfc1PZTbx444vkm/L5173/SiR5dg5E7XJh33Q3gZdeIlpbS9nCXEw2LdXbz+8AfmfjCHqLBk3DXlnqu3Yd25q3YVabuPLkG1C+Dqxufre7lVyLluvmfHx5pk6t5L4rpnGobYR9pz3yh+bSr0Lr63w+azYKQcEjJx4ZNVDaQPzkSYwjrVhy9JyuvvgkM5Ik0XPSS0GFjeRo9Ixp1So6/PLf2i1pGMB2QRWp+Vk6vGIBfpOEdvSD+d3zO9HR7p8q4MFvhHTKgvsCKVILbQZ6pWxyxRQ+gyz3DQ9MXCdlTGGgToQQ9Gm6UvaML1KL7GdjaLwmUCcDJBMSidg7Z5eP7+khnZKoWuMmuOM1kt3d2Ee7qNvbt9Mb7uWewT4oXc2vGnU4TFo+Pef91SuTvJfpLjMnUvmUJpMgCKTyHegiQwQ90fOu3BlzktV31qEuLqI63gzA3JF+0JhIu+awu2mIVWUOFIqJ2XRY/3bJb/k6AJaJGjwxDwMVspFTtuBhqCs0YXPpPU0+ECBvWhahPXvRz5mD0mplZ8dOZmfPxtVTC/EAVH1OvsOU1ZBOcplgIJFOcFI9hConR55LLTYz1BUinZ54x/LxJi2lebX1JVZGohzXrSItwdWjRepgR4CGN7qpXO3GWWwBQFAIzF5VwOd/sJj8qVZ2/6mJbT+r5nMVLqJJkaePDcBl93BPVxORVIQnTj0xkac3yQXKZJH6t6BUw9U/Ak8zHNnM+tL1lNvK+WXNL0mm5YtQpclE1oYNBLZvJ9Hdg9agpmJJHk2HBy4a+WYimqL65TayvY24ptnI/bfvv2cnfzg6zL3b72VHxw6+veDb/GDpD7Bqrfxw+Q/pDHZyf/X97zg++94voHQ4GPivn6BQCMy9qoi+Fv95m32R0hJdJ0YomjHq6jtlCsmSPHZ07GCttQJdZBgW3ktDj599pz1sWl6KRnVuL59bFhaSZ9Wd7aZedi9orTgPb2F96XqebXkWf9yP5dOfQWEw4HviSabNd9J90kssPHGyyvOBbyBC2J/AXXE2esa0aiXtgXb0CCi1hYBwwTj7ghxDkzAVgTGNQkqh1b63k3qmsPIP4jdCPGnHneGF1Rhum54+KZvclIjHNFqkDk7cLvmY3FeTDKLRjmWkZvZjWXimSE0xYhbQJuSC5e2zvWIqTcObPRTNtGNzGRl56CHUhYWYr7wSSZLY3LCZqbocVnt66Jt5D6+fGuL2xUVoVZOxM38tFS4zTWk3pUl5cyCQY0Q70k0qkT7vEvahziAanRJFwwH0lVXUDNZQYCrA1XUYipZyfCCKJ5yYEKnvGHlWPfOLsvhLXR84pkH2NJYOyA7I+4VWVC4X5oFGpLTEQHtgQtbY2+wju8CEKh4k1tCAceUK+sP9NHgauLL4Slnqa3RCySr5DkVLQalhvldWuRwdrEY/d45cpBaaScVF/IOXnnlSzWANQ8kAV8dEHh0spSBLz6x8C+m0xBt/PIXerGHx9VPwPLiZ4d/+Diklv2bMdh2f/vocrtw4g5HeMNW/P8ENehOP7GsjPX8jFSKs1OTwaOOjRFPjY0g2ycXDZJF6jqTG3GbL10LpKnjj/6CI+fnGvG/QFexiW/O2M8fa77wDBIGRh2X31srLCxCT6YsmjqbmxSYScYky714KfnE/Co3mHT9v9jZz+4u30+Rt4ueX/5y7Z999pohd6FrIHTPu4PGTj3Og78CZ+yhNRnK+8XWiNTUEt7/KzBX5aI2q89ZNHeoKEgslyS9UEzl8GMvaa9jesZ2YGOPGgU6wlcKUK/jd7lZMWhW3LS465/9rrJt6tMPL7uZh0Flg0RfhxF/YmLeaaCrKU01PoTQZsXz60wRefpnScj3ptHTRufyO5aMWjBap6uIiNMXFdAQ6KEql6VEUkm3UkG26MJx9x1A6StHo5Q9xg0Z8T5EaG70A1kb9+IwCUdGJ4wI5R51aidFsxSzpGTGJKFNRIp7whK1nrJgQ0mHMKpE+yU5+1kdHJkwkbpuesGDAhh6vCTQJ+QI/EjhbGLUcHSQSSFC1ppBITQ3RY8ew33UXglLJnp49NHubuTsYRpFdxm97pqBWCty+5Nzfly5FynPNDAh29IKeLEFNv11AM9AKnH+H36GuINkuLWJ/P7rKSqoHqplvnwHDTaPRM7JyZmXZxBWp8C7Jb/lacjsOMM06Rc5LXbgQXc1OAPonYC5VTKXpP+2Xpb5v7QNJwrRy5Rmp75W5i6FpO8z+LChV8qawxgjuRWS176fMVjZqnjSXZGcndqvcDR68BOdSX23bjlaSWJ63jDdOB1g724UgCBzf3cNQZ5AVG8pINzcy+NOfMvTzn9Nx990k+/sB2fRy+tI8Pv+DxRTNtFPWJ7K8I82r9QmYdQP39rbijXvfcV2caSSiqfd14Z9kYpksUs+BY10+Vv/0DU71B+U5xWv+E6I+ePO/WeVexdycufym9jdndo3UeXlYr12P7+lnEP1+svNNFFTYaHiz54LP5IoGohzb0UmOp46Z//dfUDvfGTj+Vs9b3PXyXSTTSR5a95C8s/ku/n7+31NiKeHf3/p3gomzHw5ZN92EtqyMwf/5H5SCSOXlbtpqhxnp++QviDsb5XlUW89RSKcxr13Lsy3PMtXkZnbnUVh4L93+GC/W9/H5RYVYdH9bHMCGywopyNLzs1dPyR+cS/4OVDrK67axLH8Zj514jISYwHbrLUixGOojO7E4dLQcvbiK1O5TXkw2LWaLgsjBQ5hWrSYpJjk2WMOMaJjGpOuCkvqOYXUWY9DJr22dEHuPu++ZTmoyRNQgkdYXTpik71xw2/QIkh2fSZ4FjfgncCY1lEBBmog2jiMtd1Izfb5Xp1bisuhQ4SCoB5Uov6dFRudSJUmiblcXWbkGimbaGdnyEAqLhazP3gjA5obN5GptXNt9kuhlX+ap6l4+U5WP05zZxXmmodcoKbYb6VEVU5pW0GaOoRt1Ij2fWalpMc1wd4gstfx3909z4ol5mCeNblSVyPmolQXWCd+8Wl8py8dfqu+To2jEBMt0eVQPVKNeMBfFYDc2h5q+CZhLHewIkkqmyS+X51GVNhu6WbPY1bmLKdYplPbUghiHyg3EUyKrf/oGf9jTKkt+++tZYJ9FzWAN6spZAOj6mlCqFZfcXKqYFtnRJkt9T5svJyGmuWaWi7A/zoFnT1M4087UeQ76f/xjlDkOXP/7P4g3nqDthhsJvv76md9jtGpZ95VK1myaiV0SaH60hcPxu5kTCDBXn8fW41vPqAwzAUmSGGgLsPPhE2z57l6eu7/mo+80ybgyWaSeA8V2A5FEiu8/Wy8XGK5KmH8nHPodguc031zwTYaiQzx+8vEz97Fv2oQUieB94kkAqq5wE/LGaau7sM1w9v34aVKChoWfLkVfVfWOnz1x8gnu23kf+aZ8Hrv2MWZlz3rf36FT6fjxih8zEBngp4d/euZ2QanE+d3vkuzqwvvHx6i6wo1KraDm1U++m9rVOIKj0ERi58topk6lx6mkdqiWG9N6BJUO5t7O5r3tCPCJxDtoVAq+ceU0arv97Do5CEYHLLgb6p9kY/E6hqJDvNT2ErqZM9FVVuJ76gmmzMuh++TIRSP5ldISPad8uCtsREejZ0yrVnK4/zDBZIg1kSiHg44LSuo7RnGOGZ2gJaQDbdL/XrnvmNlPIoRClwZLwUQs85xx2wwMiw5EI6gTQWLhiduBjoWSaKQYPqNEjpjGp8zGYcz8rnSh3YBHcpKFQFovPz/GslL7WwMMdgSpusJNsqeb4GuvYbvlFhRGI7VDtRwdOMqdKR1qvY0/xZcRToiTsTPnSIXLzEkxn9JYmBN6L7qYvGF5Pjup3v4IYjKNOdAJajV1WXInfb5/ELRW/Fkzqe70TUj0zLvJz5Ilvy/W9UHhEtBlsSzoI5FO0DJF3hTJVgfobw0gjfMsZ2+zrMTJm2ohtPctjMuX408EODJwhCuLroS6J2UVVMF8djQO0DkS4bljvTDlckBigaQhmorSkacClYp4fR0Ot+mSc/itGaxhKOHnmkiMx73TcZg0LCi28dZTzYgpiVW3lhN44QVitXU4v/VtbBs2UPLM06jy8uj+u68y8F8/QUrI712CIDBjsQthXR6nVCkO7U7wdOBX3N6tpzfcyyttr0zw2cpGn8f39PDkfx7m6Z8coeXoIPYCE4MdQXyXoNQ7k5ksUs8Bm1HD99ZN53C7l2eq5RxErvg+qHSw499ZkLuAFQUreLD+QQKjMi7d9OkYly1j5JGHSScSlFQ5MNt1F7SBUv+Tz9HsyaZA7+H/s3fe0W2W5/v/vNrbsjzkHdvxyHSWs+OELDJYYYSRABlQoGWWLkopBQoUSvsr8GVTsimQQMMKSSB77+XYjme8t2VrWFt6f3/IDquUhDiJneZzjs8JOtI7jCw993Nf93WlzLvm1OOBYIC/7v8rT+99mrHxY1k2Yxkx2phvv7i1Atb+DuqOApAVlcXCAQtZXbKarVVbTz1NlzMO7bhxNL/+OnJfO33HxlG0rwFHa9ftcnvdfupLrSQkqzukvtP5pPQTpIKUK0r3Qf/rsKLn/f2VXDUorsvm3a4bmkCSSfP1bOqY+wCB0cU7SA9PZ1n+MkRRJPzmm/CWlJKgsRAMiJT38I2NTlpqHbjbfcT3CUl9BaUSzfDhbKjcgFoiZ7TLTZ4vhvQe2ElNjujMSgVJeyMuu4+A72vVhMvhRRDEUC6jMoDSlHABr/bMSQxXU+Y1EkEQWdCB68I1UnE5fMj9Dto0Avqgkkijrkd0pXuZNJT5TMT4/Dg1HhBF2juK1GObqlCoZWSOisGybDlIJITfOheARbmL0Mu13FC6n+CwO1i0r4HsXuEMTLgwDrA9ncwYA4fdZlKcdgq1NmQBD0p54Jw6/H5tmnQEVWYmh9qOY1QaSa08CMlj2VXWSiAoXtB51G8yc2As+XU2yls9kD6VYSf3oZAo2C4WITObMbScwOvynxOV03+jtqgNU5wWoaqUQEsLupxxbKneQkAMMDlyEJzcBlk3giDwwf5QHFhujZVGQz9Q6Mm2hEauDlhzUWVm4jpyhKhEPc1V9vNecF9Iviz/EqUIY6OG8UWxk6n9zNScsFB8oJFhM3qh14g0/f3/ocrKIuyaqwFQpqSQ/P57hM+di2XJEsrn3oq3qurUMeeMT2Wt3k/7iHBcQhRlRfcxrfYmlhxdeioB43zTUuNg63uFLHlkJ1veLUQMiky4JYMFz41l2s9CTZSyIxeXWq2nc6lI/YnMHpbI0CQjf/migDanF/RmyHkYCtfAyW08OPRBbF4bS44vOfUa08KFBJqasX32ORKJwIDL4qkpaqO52nHhbuQn4so9zoHl+whIleT8cuqpx50+Jw9tfojl+cuZ23cuL098Ga1c+/ULvU7Y/Cy8OgL2vgH/ugnsobmGnw/6Oenh6Tyx+wna3F/Pt0T/9jcEHQ6aX3+dwVMSEUU4svHrD8OzpaawlWBQxNR2AkQRzeVT+Kz0M3K0SUS67TD8TlbsrcDpDfCznNQuO69cKuHByenk1dpYn9cAYQkw6CaEw8u5PXUWxa3F7K7djWHGDCQ6HdKNH6E3qS4al9/qEx3zqBnhtG/bhmbUSESFnE2VmxinNKNASoXYs+JnOkmO0BLwmWgxCEitHTE038jBdDl8KGVBBEQUqiD6qJ5VpCaEa6gORmD2eRFpx+O/cGY9LrsXmceGVQuyoK7bO/t2kmTSUOQJJ8bnxaL1owi6cNq82C1uSg830W9sLFJPO20ffUTYFTORm82UWcvYXLWZm+UxaAUp24zXUGVxXeqingV9OsyTkn1+rFoQ1Uo0gutU9uO5oKnSjkwhQXZsF+qsgRxuPMzg8D4IreWnpL56lYwhicZzdg1nQqfkd01uHWRMR+VsYVhYGrvr9qAZMQL18ZDp3fmU/AYDQepKrcSlG2nfvgMA7dixbKzcSIw2hn7VuYAIA2dTZXGyvbiZaf3NAGwtboXksURW7CHZkByaSx00CFduLpGJWrzuANZz+P+/OxEIBviqfC3jnU5qTRNp9waYmhnNtveKMJo1DL28Fy1vvom/qYmYPzyKIPm6bJAolcT88THiX34Jb3k5J6+9Dtu6UKc0JkzF9P4xvFvVxLWPDqePbhcpFWMYsv061u3Z+kOX0+X4fQEK99Tx0V8P8v6f91Gws47UwVFc/9thGb/smAAAIABJREFU3PTYCAZMSEChlmGIUBOVpOfkpSK1W3GpSP2JSCQCT88aSKvTywvrC0MPjroXwpJg3aP0MaYzI3kGKwpW0OwKdb60Y8egzMykZfEiRFGk39g4ZHIJuVt6VjfV39xM6UOPUB2bQ9oQExFJoS/S+vZ65q2bx7aabfx+xO95ZMQjSCUdi1dRhLzV8Mpw2Po89LkC5n4Ibit8cCv4PSikCp4d9yxt7jae3fvsqfOpMjIwXn89rf96D5WjgfTsaPK313aZ7LUy34JMKUW5Zw2KtN4c0DTQ7Grm2uY6iB2MxzyIJbvKyUmPpF+coUvO2ck1g+NIjdTy4oaikO392IfA72FmXQlR6iiW5i9FotEQdvXVONavI6W/gcoCC56LYMC/prCVsGg1SnsD3ooKdDnjOdZ8jBZ3C1P8EqzqBPzIeqTcN86owuU306IHZXPo7/ubc6luuw+FxEcQkMplxJt61j0mmtTUipGY/QF8EjteUXHBOg8uhw+5sw2rFvy+sG7v7NtJUkTI4dccCFCv8aDwWHHZvBzfWgOiyMDLEmhduRLR6cS0YAEAS44vQSGVM7dkPwy8gTcPOYkLU51afF/izPna4TcUQ+OJMaH2WLC1nMNOapUdU6QMsd2Bv08q5bZyhgoaAMTkcWwtamJcWiQyafdYosUZ1QxJMobmUtOmgETG2ICUUmsp/kGZKGqLUGkk5zUvtakyFHsTl27EsWM7qn798IZp2F27m0mJkxCOfwixgyEynVUHqhAEePyq/kTrlWwpbApJfi1lDAvP5FDDIZRZAxGdTsJo7Tj+/4bk91DjIZo9bVze7mSVYxB6pQx1STvWJhfjb8kgUFuFZckSwmbNQj1o0H88huHyy0lZvRpF71RqHvoldU8+SdDj4fbRvbC6fKw/aWPSdJGZpqdQBtWULQ2yfWXROY0tamtwsuPDYpY8spMNSwpwt/sYe0Ma858by5T5/YhJDfteCkXKoEjqT9q+taF8iQtL9/gE7KH0izMwf0wK/9pXyZGqNpCrYMqfoCEXjrzLvUPuxRvw8taxt4CQVj9i4QK8JaW0b9+OSisnY4SZor31PWbOUPR6qX7wIcp0wwhKFYy8NhOAgpYC5q6ZS6WtklcmvcKcvnO+flFDHiy9ClbNB3U4zP8CblgE6VNh1utQvR/W/ApEkT6mPtwz6B7Wlq9lXfnXswtRD9yPRKGg8e9/Z8jlvfB5AqHFXBdQlW8hrpcGz4F9GKbP4OOSjzHJ9eTUFcHwO/nkSB1Ndg93je+6LmonMqmEB6ekc6Leztrj9RCZDv2uQXFgMXPSrmNX7S4KLYUYb7oJ0ecjuuUoQX/Pl/wGA0Fqits6ome2A6HomY0VG5FJZOTUFlEuSyVar8SoUfzI0bofMqkElL1p1YOmrQH4dgyNy+FFHnThUIM8qO4x3b9OEr6RleqUWhEFCR7nhdk4cds8KLx2rFoBmye85xSpJg3VYhQx/gD1Gj9ydxvWxnbydtSQMigKvUFK6/IVaEaPQtWnDw3tDXxW9hmztKlEeByUpc1jd1kLt41O7jbFTE8kOUKLRRZJZFCBDIG2SBVKWx0Oi/uc5GWKQZGmKgdGaagIKumItR1ibQa1iWKhF3VWd7eYR/0mVwyMJa/WRnm7HJJGM7o+FEWTmygiAJEaJ3Xn0eG3pmMeNSZWjuvwEbQ5Oeys3Ykn4GFyWB+oPQwDZ+MPBFl5oJoJGVHEG9VMzIxmW3ETvl45AAwLyLD77NSnhuTyquoCJDLhf6ZIXV++HpUIOca+/LskwOVJERz5qpKMEWYS+5hoeP6vCHI5UQ//8r8eR5EQT/KKFZjuWEjbe+9TfuNNDBLb6BOjZ+muCsRhC0hRHiU+51PyzNs5tqma9/+8l+oOh/+uIBAIUnKwkU9ePMy7f9pD7qZqEjJNXPPQYOY8MZLBU5JQ6X7Y9DJ1cBSIcPJoz15fXUxc+mY7S345NZ0onZLHPs4lEBRhwPWQMAI2/pleShPXpV/HqqJVVNtD3RTDjBnIzGZa3lkEwMCJifh9QQp21l3I2zht6p99lrbcEmrjx5M5KgajWcPmys3MWzcPiUTCshnLyEkIffjjtMCaX8Mb46DhOFzx/+DurZA89usD9p8FOb+Gw8th/z8BuGPgHQyIGMAze5451YWWRUUR8bM7sX+1AU1dAUn9Izi2uQq/9+x24qxNLqxNLqL81SCKiBNHsaV6C1eJGuSqMIL9r+Ot7WX0izUwLi3yrM71Q1yZFUd6tI4XNxSF3kM5D4PHxmybDbVMzbL8ZagyM1APGYJ0zXJ04UpKDvZsyW9jpR2fOxCKntm+DUVyMvLERDZUbmBUWCZ6RyPrgyN6pNS3E2l4Cl6diNITWrh9q0i1+5B6bbRpAb+u27vRfpc4o4o6Qlmp7YqOrFTb+d99DviDeD1B5D4HNo2IzRfVY/JmQ0VqJLF+f0cMjR1LnQtPu5+sSQnY1q3D39hIxPz5AKwoWEFQDDKvIg+Sc3izUItKLuGWEYkX9kZ6OFKJQLpZT50siV7IqAsXUTSVEwyI3zM86wraGp34PQH01nIkej37FDUopUr6Vx+F5HFsLWoBYHw3K1JnfFPymzmT9IZCopQmtoonkJnNhFnLsDW7z1sXqra4DaNZg5h3AAIBdDnj2Fi5EaPSyNDafECAAdezrbiJepubm4eH/k4m9onC7vZzyBkD2miyLaHN7kOyGqTh4XiPHSEiTvc/UaQGggE2lH9JjtNJS/QkLA4v/er8yORSxt6QjmPHThybNhHx83u+l9zwnxDkcsy/+Q2Jb72Jv7GR8tk38mCwhPw6GwetesicybWlW8jvu4mTE7YgCAKf/OMwW949cVbxL7YWF3s+KWXZ73ex/u3jWBtdjLwmldv/Mobpdw0goY/pe13T/4QpTktYlPqS5Lcb8aNFqiAIekEQ3hUEoUQQhC8EQYgVBOF1QRCeFgThho7n5AiC8HdBEF4RBKFvx2MPCILwiCAIr3YcQy4IwouCIDwoCMKfz/WNnS/0Kjl/vLIfx2tsvLu3IhRJM/0v0N4IO/7B3Vl3IxWkvHbkNQAEhQLT7bfj3LsX1/E8IhN0xKUbyd1afU52bbuS1g9W0vb+BzTMeAgRCcNmJLMsbxkPbn6Q3mG9ee+K98g0ZUIwAPvfgf8bBgfegew74P5DMPwOkPyH2bWJfwjlza57BMp3IJPIeGbcMzh9Tp7c/WTIVAgwzZ+PzGym4fm/MmRqIi67j4JdZ1fcV+WHFgS63K9QpqezXsjHH/Qzq/I4DJ7LlpMOShod3DU+9bQ+5H4KUonAQ1MyKG508PmxWogdBGlTCNv3DrNSruSLk1/Q6Gwk/Oab8FVUkBQboCrf0qMzvTrzUeOSNTj37kM7Poei1iJqHDVM9gYR5RpWWvuS3gOlvp2oo3sjVQeQ+V3IZN+W+7ocXqTuVqxaAZ/fRIyhZ0WHKGVSBF0MUQERuypUpDobzn9OotvR4ZLscyCqRRrEiB7TSTVpFQQUBkxBORY9KDtM9iITdcSmhdGyeAmK3r3R5uRg89pYVbSKacZ+JLbVYB96Dx8fqeHaIQk9UmnQ3cg0G8j3xZHsdlOqc6JuD20Cnou51FOmSScPox44gMNNRxhoTEdurYKU8WwtaiLDrOt27+N4o5rBiR2S38zpCMBoZRS76/egHp6N5sROgPMi+Q0GReqK20LRM9t3INHpkA3ox7aqbVyWcBmy4x9BSg4YYnl/XxWROgWT+4Yk8WPTIpFJBDYXNUPqBGLL9xCvi+NA51zq0aNEJelpqrSfWntcrBxqPESLp5VpjnY+8QxlYECGu9rJ6FmpqNUCDX/5C/KkJEzz5p3RcXXjx5Py8WrU/fqR/NYL/O7IB7y7tRBG3IXaZWGucSDrvasZeq+RwVOTyN9Ry3tP7aXieMtpnyMYDCnKPn/1KMsf282hdRVEJxu44t4sbn16NNkzktGGnZnLuyAIpA6Oorqw9aIYqboYOJ1O6mTgPmAAMBpYAuwD/gy8JQhCFPAu8Arwb+BtQRD6Ab8URfE5oAX4HXAHEC6K4kvABEEQpnTxvVwwrsyKZVxaJC+sL6TR7oaEbBg4G3a9gtnnYU6fOXxe9jlFrUUAGG+cjUSrxbJ4MRCKo7G3uLu1hNN56BD1Tz+NMO5yytrNZIw283+lf+eFAy8wpdcUFk1fRKQ6Eip2wVsTYM3DYO4Pd2+HK/4GGtMPH1wigeveClnFr7wd2ipJNabywNAH2FK1hU9LPw09Ta0m6pcP4T5+HF3RTswpBo5sqDyrrNnKfAs6oxzJgS3op03j45KPGaCIIM3jguyFvLm1jLgwFVdkxf7kc5wOMwbE0CdGz0sbivEHgjDuYXA2c1tATVAM8t6J99BPm4YkLIyIki0E/EHKc7vv++XHqD7RSkS8DjH/MKLHgy5nPBsqNyAgMLH8EM5eU7D45GT24E6q2RyLTB2SwqmVQdpbQ0VqMBDE0+5H1m7BqgWJ1Nwj5ZpxJh1KUU+bOrTodtRazvs1nMqb9TqQqQLUYyLO2DMKfkEQSIrQIRfDadUJKHyhIjVrYiKuffvwFBRgmj8PQSJhZeFK2n3tLGiogog0ljVn4PEHWTA2+cLexEVCnxg9x7yxpLgdHTE0ocXyuXD4baq0I5UJyE7sRdq/LwWWAoZIQptxrvjR7Dtp6XZS3046Jb8VohkiMxljs2D1WLH2T0RbnYtUJpwX86SWagded4C4NCOOHTvQjh7N/pbD2H12Jut6gaUUBs6m0e5m44lGrh+WgLzjM1avkjM82cSWwkZImQDtjQwzpHGw4SCqwYPwlpURGSXD4/RjP4dzyd2B9eXrUSMwTpPAR8UqJnsURCcb6JcTT+t77+EtLcX8yO+QKM58I0xuNpO0ZDGRv/gFEyoOMOOVR6i1GCGqDzdVHEUj07CkaDFjr0/jut8OQ66S8fkrR9m4JP+/jr+1Wz0c+OIkyx/bxZrXjtFUaSd7RjK3PTOGK36RRfLAyLNyd08dEkUwIFLRg9dXFxM/ujISRfFjURRbgXDgMJAN1Iui6AH8wBwgEajv+BlDqLDt1CPWA9OAqR3//uZj30IQhLsEQTggCMKBpqae024XBIGnrumPxxfkL1+cCD04+U+hruqGJ7lj4B3o5Dr+7/D/ASDV6zHeeCO2devw1dSQMigSXbiy2xoo+RoaqH7gQeRxsdSOuQNEWG14m5VFK1k4YCF/m/A31O0W+PAOWDwDnK0wewnM+wxiBnz/eIEgqw5Ucf3ru/j0aMgCHlUY3PIeBHzw/lzwOrm1760MjR7Kc/ueo7499NYJu/pqlP360vTiPxg8MRZbs5vSQz/tvRIIBKkubMWsbEMQRZpGp1PUWsS1rc2QMoGjrij2nrSwcFzKqS+4c4Wko5ta1tweynHrNQYSR5G4fwmTEyaysnAlbmkQ46xZyDZ9iNYg/8n3faEJ+EKujJ3zqIJKhWbEcDZWbmSooTcRjiZKo0OO0T0xfqaT5EgdqEI7uSrBfUo62JkpqrS3YNWCUpV8oS7xrEg0afB7TbToOuS+jefPNKWTzrxZuc+BShGgTjR1uw7UfyPJpMblj6JVBxHNufSOtJI+PBrL4iVITSbCrr4aT8DDivwVjAnvR9+aXAIj7mH5nirGpUX2aDl8dyIzRk+JGE+Kz0+NMYjK3QqI5yQrtanKTni4BInPS10vHQExwFB7K2ij2W2LxBsIMiHjx6WVF4IZA0NRcms6uqmjq3IBOBDnRiIGMGk956VIrSkKKXGiFG346+rQdkh91TI1o+uKQKqAvlfz4cFqAkGRm7K/LYmf2CeKE/V2GiJHApAdlNLqacWaFuq26l0hCfDFLPkNufp+SU57O+3xl5PW4EfuF7lsTibBtlaa/u8VtGPHops48SefQ5DJiHrgfhQvvobW68Iy/zZaHSMw1OZyU9x41pevp8peRUxKGDc9OpzsmckU7WvgX0/upezw1+sbMShSdcLCurdyWfb7Xez99CTGaA3T7xrA7c+OYeTVqehNXbM5aU42oDEoKDtyqUjtDpzWylsQhDDgBuDq77xG7Pj55n8L/+E5QsfPdx/7FqIoviWKYrYoitlRUd1zJ/GHSI3ScfeEVFYfrmF3aQsYE2HM/XD8Q8Iai5g/YD5bqrZwpPEIAKbbbwNBwLJsGRKphAET4qk+0Yql9vzmjP0YQY+H6vvuR3Q6MT7zIicONFMef4Sd1i08OeZJfpn1cyTb/x+8kg0Fn8GE38F9+6H/taEi/Ru4vAGW7DzJZS9s4TcfHqOw3s7DHxxha1HHh1FkOlz/DtTnwqf3IxUkPD32aQJigMd3Po4oiggSCebf/g5/bR1hh7/AaNZw6MuKnyTLaSiz4XMHCCvfgzIjg9W+/SgFGdObq2H4nby1vQy9SsbNI5K64lf5o0zrb6Z/nIGXNhbjC4qQ8yuwVXO7Ig6b18bqktUYb7oRwe8jTtlERV4LXnfPk6TUl1kJ+IKhfNTt29COHEmVp57i1mIm+wWQa9ktGQLQo+W+yRFaRAw4VCDztZ2S+7rsoe6f0mnDpQZlWK8LeZk/mYRwNQ2+cOx6J4hBnC3n/7OrU+4r9dvRKwL4NWZU8gsXh3OmJJk0lHtN6GUiciwMVhcQqCzHsXUr4XPmIFEq+aTkE1rcLSx0uEEdzjrZROpt7ktd1C6kT4ye4mA8KV4frXoQ5AJqqQ97F3dSRVGkqdKBURIq5I5GtiMRJAyuzoWUHLYWNaOWSxmeEt6l5+0qEsI1X0t+M2Zg8nvpq4llUyAPmdmMsb2S5ko7vrP0ivgxaovbMESpEQ/vBkAzdgybqzYzLm4syryPIf1yRFUYH+yvYkSKidSob3+PTMwMbQJsrFOBKZVhzaFIuyMRDhAEVBXHkUgEGi/iIvVgw0EsHVLfLywjGOyVkpkTF5I6v/QyQZcL86O/75Ixp7Rpl7HizmfJj+pN/eIN1OyNYk51DVJBytK8pQBI5RJGXp3KDb/PRhumYO2buax76ziHv6zk3Sf28OmLR6gpbCNrciJznxzFNQ8NoffQaKRd1EAQAwHEYBBBIpAyKJLKvBb8vnP7Pr7Ej3M6M6ka4EpgMdDpHJMgCIICUADvAzVAAhBDSAq8AYjveG4MsLHjsYTvPHZRce/ENBJNah7/5DhefzAUJ6Izw/rfc2ufuZhUJl469BKiKCKPjcUwcwatqz4kYLXSb1wcUln3iqMRRZH6Pz2BOzeXuL8+z+ZDFvz4ORC3jjemvM51ASW8NhI2/RnSJsN9+2Dio6DQfOs4VpePVzeXMO75TTzxWT5xRhWLFwxn1+8nkW7W8/MVBzlW3THPlnE5TP4jHP8Qdr1MoiGRX2f/mt11u1lVtAoA7aiR6CZOxPLWmwwaY6K5ykFVwZlLDasKLAgS0B5ci2baVL44+QWTUWPQxlAZdRlrc+uYMzIJnVJ21r/L00EQBB6emkGlxcm/D1WH3I/NAxl8eCWDorJYkb8CWXIvNCNGEH7oMwK+4BnNcHQXqgtbEQSIUtjwVVSiHZ/DxsrQx8HkiqOQOZ2CZj+xYSoMqh924uvuxBlVBPwmLHqQOZtot3oQgyKuU3OUdkR1EHXk+dkE6WoSwzXUipHI1T7kvnZc1vMvjeuU+7pl7RhEGRHh3SNX8nRJitBSEYgkxufDbpDjb2jAsmQpglJJ+JxbCAQDLM1bSv+wNEYUbYVhC/jn3gaSIzSnFtqXOHui9Eqc6ljiAjJEQcBlDkMdsHV5J9XW7Mbr8qNrLUMWF8sebyEZ+l7o7PWn8lHH9I5AKeu+Gy1XDIzleI2NSs0AUJsY65dwrDkXRfYQtKV7CQZFGstt5+z8YlCktqSN+HQj7Tu2o0jrTYG8mWZXM5NVceBogKwb2VNmoaLF+R+NxdKiQ3nKmzskv4kV+4lSR7LPnosyPR1v7hHC47Q0X8RFakjqK2GsJIKaYwa8cgnjr0vDXVBA28qVmObOQdm7d5edb/bUgfxuxEKa59yJvVKB46UDzA9ms7p49SmDTICoRD03PJLNqFmpnDzWxK5/l6AxKJiyoB/znhvD2OvTMJo1/+VMp4+3qorW99+n+v77KRo1msp584GQy6/PE6C6oOuchy/x0zidLYgHgRWAHTgJPASMAB4F7hFFsRGYC9wLXAssFEUxD/i7IAiPEipsnwHeBtoEQbgP2CGK4tquvpkLjUou5Ymr+lPc6GDRzpOg1MGkP0L1fjSFa7k7624ONBxgV+0uACIWLEB0OmlduRK1TkH6CDMn9tThcXaPOJrW5Suwfvwxkffey+ZwP/WHnFQkHOHtCb9j5Mbn4f05IFPBbR/DTSsgPPlbr2+ye3h+3QnGPbeJF9YXkpUQxqp7RrPqnjFMzIzGoJKzdMFwTFoFCxbv52RzRydm3MPQbxZseAJKNjA7YzajY0fztwN/o8oW2vGM/s2vCbrdGHe/jzZMwaH1lWd8f5V5LURoPcj9Lo5nGbB5bcyqLYFh83lnVyVSicDCsSln+Vs8Myb1iWZQopGXN5bgDYiQ80toKWaeoT/Vjmo2VW3CeNONaEv3olZDaQ90+a0pbCWqlwHvvpDRhm78eDZWbqSvNoE4RzNi/2s5VNlG39iuzaQ938ikEuSyBFr0AjJbHUF/qEDt7KTKfQ6kyiDG6PgfOVL3JCFcTZ0YgVHiR+534Go//119l90Hoki7sh1FQNPjonySTKGs1JhAAIs2iKewEOsnnxB2zTXITCY2VG6g0l7JQlGPIJFxPOFmDle2MW9M8lnNXV3i2wiCQGasgRYhgUikWCIUqJ2NXT6T2CkfVZUeRDVgAEebjjJEGvqcqzZmU97iZEJm91aRnZL85jVC+uWMqS/GL/qpzTChrzoKcE4lvy217Xja/cT20uDcfwBdTuj7QyaRMb6+GJQGSJ/GB/sr0atkzBjwfT8JQRCY2CeKnSXN+HrlIHjtZOtTOVh/EPXgDvOkRB1NVReneZI/6GdDxVdMcLrIlfwMowfCxkQhV0qpf+YZpEYjkffe26XnnJARTWKEjpejR9Pr1ecRgyJT/7qDqXvcrMhb/q3nSqUShk1P5tanRjPniZFc9+thZI6MQXaWKpmA3Y59wwbqnnySksunUTr1cuqfeBJXXh7K3r1x7t+Pp6yM+MxwFGoZZZdcfi84pzOT+hdRFIVv/CwVRfFnoig+IYriBx3P2SqK4kOiKD7QUaAiiuI/RFF8VhTFe0VRtImi6Ov49yuiKP7hXN/YhWJyXzOX9zPz0oZiatpcMHgOxAyEDU8wO+VK4nXxvHToJYJiEFXfvmjHjKZ12XKCXi9ZlyXg9wbP2rG2K2jfs5eG559HN3kSq3PkbFx9FFEi8qu+baQunw3VB2H683DPDuj97ZmFKouTP358nHHPb+LNraVMyIxizQPjWLxgBMOTv22gFG1QsWzhCETg9kV7Q8ZTggCzXoPofvDhQgRLGU+NfQqZIOOxnY8RCAZQpqYSftNN2FetpP9gDTWFrTScwe6ty+GlsdKOqek4ysxMPnTvIlaqZqTHT1vfOaw8UM01g+Mxn2fX1c5uak2bi5UHqkLFuimVSXnrSNAlsDRvKfqpU5GZwjG7S6k43nJOA7G7Gq/bT8NJW2gedft2FCkptJoUHGs6xpSAHBQ6DiuyqbQ4mTEg5kJf7lmj1mbSYgClJfQ33d7m+dqR1usAlYQ4U9iFvMSfTGJHgWUO+BGCDtwXwGPE5fAhF11YtRD06XvUPCp8MyvVT4PWj7eiAtHrxTR/HqIosuj4InrpEpicvwEGXM/bR5zolDJuGJbw4we/xBnRJ8ZAni+WFK+PamMARUsVjjYPAd9PN+b7Lk2VdiQSUJUdwZYWg8vvYmi7DfRxbGwISVK7q2lSJwnhGgYlGlmTWwuZ0xlsbUYtUbInxoHc345B46e+7NwVqbUd+ahGRxmiz4d23Fg2Vm5kpHk4+oIvoO/VWH1Svjhez7VD4n9Q/j8xMxqnN8BBYSAgMMwv0OhqxJWZRNBux6Tx4rL7cLSe/2itc02n1Hdiq5RDpQMokwWYMSMV+9q1uA4cJOqhh5AaunaTWCoRuG1UL/aXt3Iyczyp9/RDlxBk3sYg0U8toq2x6nuv0ZtUhMdof/I5Rb8f5+HDNL3yKuW3zKFo1Giq77sf2yefouzdG/Njj5G69gvSNm4k/uWXQCLB+umnSGUSeg2I4OSx5rMy5rzE2dPzLCV7AI9f1Q8Rkac+ywtFrkz7C1irkO97i18M/gUFlgK+rPgSANPCO/A3NWH7fA1RSXpie4eRu7UG8QLG0Xira6h56CHkvXrxziwty3evIr05myGGjcQdfQ0Gz4UHDsGoe0D6tRyzuCE0Y3rZ37bw/v5Krh0Sz8ZfXcYrc4bSP+6HF+KpUTremZdNs93LgsX7sbt9oNDCze+CIIX35xAj0/LIyEc41HiIFQUrAIi8714kajURWxahUMs4vL7itO+x+kQriKDP34QweRy7andxjbUNSd8rWX7cg8sX4Gc5qT/9l3gWjE+PZFivcF7dXII7AIx9CGndUW6LHsnRpqMca8vHeN21hB9dg7+HSX7rSq0EgyJxKRqce/eiG5/D5qrNAEyuOg6ZM/noWDMqueRULl9PRhOZgVMrom0LyZna2zynOqkyfzsBhaLHZaR2EhOmop4IzIEAAdGG23/+JYpuhxe5z4FVCx5vWI/rpMYb1dQSSYw/QJMutBjSTZiAMjWVvfV7yW/JZ546GamvnZasO1lzrI7Z2Qnoe7AMvruSGaOnwB9HirudYo0dlaMBRLBbum73panKjtEAEtFPUWzoO35ITX5oHrW4meQIDb0ifvqi/HxxxcAYjtfYqDKNQS6RM0Ju5Cv/MWRmM+GeWupLredsDVNb1IbepEI4tBNBraamdxhV9iomK6LBa4eBN7D6cDVef5Cbhv9whvDo3hEoZBK+qvBDzECym0NqrILY0KbOd4Y5AAAgAElEQVSvzhZaT1yM5kmdUl+xeR7+oISa3mpilAINL/wNZd++GG+4/pycd3Z2Aiq5hOW7K5Be9gsSRtUh3jqOAaV+yq+9HufBg2d9jm9JeEePoeKWOTS/+ipiIEDEXT+j14rlZOzZTeLrr2G6dS7KlBQEQUAeHY12zBhsn36GGAySOjgKt8N3TjdcLvHjXCpSzwEJ4RoemJzO+rwGNp1oCOV19bkSdvyDK6KySTOm8erhV/EH/WjHjkGZkYFl8SJEUWTgxARsTS4q8i5M4RF0uai+7z6Cfj+v3qzjo9q13Fx5JQrBTXavArhrM1z9MmgjT73mSFUbdy07wNR/bGPt8Xrmj0lm228n8tz1WaREnt4X7pCkcF67dSgn6u3cs+JgaKY3PDnkEtxcDP++m6uSr2Bi4kRePvQyZW1lyEwmIu+5G8/WjWSmQemRJtoanKd1vsp8CwppAIOtgh0ZAURErmltxjtkIUt3l3NZZhSZMRfGObOzm1pndfPB/ioYdDPoY5lVsheDwsDSvKUYb7wRo6UIlcxP0b76Hz9oN6HmRCsSqUBYSyGi14u2I3omWR1NqqMFX99r+PxYHdP6x5y3WeBzSVhcKqI2iMoT2v13tHlC3T+pH4kYxCfXEhvWMyJTvotcKiGgT8DsD+CTWHEJ2tP+++sqXHYfcrcNqxacvsge10lVyCSoDNFEBCQ0G0LyXdOCBQAsyl1EhCqCq09sg+QclpQZCIgi88ckX8ArvnjJjNFTJCaQ7PNTbnB/HUPTRXOpIdMkO2G0gkTCbn0D8epozI4mfEnj2F3a0u27qJ10Smg/L3RA8lhGW1uoclTDkP5oKw/icfppre/6zwJRDM2jxmUYcezYjnbECDbVbw9Fl9UVgc6MmJzD+/urGBgf9l83xzUKGaNSI0JzqakTSK0+TLjSyC5ZBRKDAVX5EQTh4itSO6W+0xt6U+4cwU6ljwlDYmn55zv46+qI+cOjCNJzs+Fo1CiYNTiej4/UYI2bgGBKpl9cFaseHoJVdFJx+zya33gTMXj63cuA3Y7tq6+oe+KJ70l4DdOnEf/iP8jYvYuUlR8Q/eCDaLKzEX4gUifs6qvw1dbiOnSIpP4mpDIJZYcvufxeSC4VqeeIO8el0jtKy58+zcPtC8DUp8DvQbr5We4fcj/ltnI+KfkEQRAwLVyAp7iE9u3bSR0Shdao5Njm82+gFPR4qH30UTyFhbx9nYatYgFPVqmhNYusISLqez6BuJDrqiiK7CxpZs7be5j16k72nrTw4OR0dj0yiT9e2Y/YsDNfLE7MjOb567PYWdLCr1cdJRgUIXUCTHsGCtcgbPsrj49+HI1cw6M7HsUf9BN+223I4+KI2vgGUqnA4a9+fDZVFEWq8lqIcFWg6pPBe86tDA/KSTD25iNLCs0OL3eNvzBd1E7G9I5gRIop1E0VZTDmfjQVu7gxJmS132AU0I0ZTUzDXspzm0/Fm3R3qgtbiUkNw71rB4JajT8rnQP1B5gcVCIoDWz2DcDq8nHtkJ45p/ldEqMjEdUCcp8dQejspPqQi24cKhCkph7lRvtdwk2RhAVkWKWbkfrdrHn54Hl1nHbZ3Ci8dmwasAaie1wnFaBXpBZ5MIzt/QXanr4XzcgR5Lfks7tuN7eaBqO0VeMd/nP+tbeSyX2ie0SnrSeSYdZTIiaQ4vNRHy6gdocWp12VlepoDUn9dZZSlOnp7LUeZag85OJ7RJqFyxfo9vOonSSaNAxKCAu5/GbOZGxT6Hu3vLcOQ/UxAOpK27r8vK11Tlx2H2ZTIGS6lxMy3RscOYDIks0w4HqO1To4UW/n5v9gmPRdJmZGUdbUTkPkKISAl6HaJA42HUKdlYXv6CHCY7U0VV1cReqBhgPYnO0kVN6IQutlv9LP5RFBWv75TwwzZ6DJzj6n579tdC/cviCrDtfC8J9B5W6uGnUVv5kPbWP60vTii1TdeSf+H4ihFP1+nIe+LeGtuf8BbJ9+9j0Jb+yf/4xh+nSkxtMz1NNPmYKg0WD95FMUKhmJfcMpO9J0Uc4l9xQuFannCIVMwp9nDaDK4uK1zSUQ0RtG3AWHVzBRFkFWVBavHX0Nt99N2MyZyMxmWhYtRiqVMGB8HFX5Flrrz0+kgxgMYv3sc8pmzMS+dh0fTpSxP6aZd+qbUAd/jUIlZfDcaSAIBIMi6/PqmfXqTub+cy8ljQ7+MLMvOx+ZxC+nZhCuPfPQ529yw7AEfje9D58ereXpNQWhD4eR98CgObD1OSLL9/DHUX8kryWPd3LfQaJUEvWrhyH/MKkxbk7sqaPd+t8LNktdO+1WL2End2Mfl0WVvYprW+oIDlvI2ztOMiDewOjUiLO6j7Ols5vaaPewYk8FDJ0HahNzaouRSqQsL1iO8eabiCn+EjEI+TtrL+j1ng7udh9NVXbiM4w4toWiZ7Y27iYgBphSUwCZM1l1tJkovZJxaZE/fsAeQEqkFr9agUQMolIEcbR5cDu8yAJ22rSgkJkv9CWeFQnhGiQBA2uyrfQ9+S5tzR42Ls47b1/qLntI7htQi9SLkT1SOp1k0uDzheNWClQOiEIQBBYfX4xWruXGk4fB1JuPnQNoafey4Dwbuf0voVPKkBgTiPdLaDaAIuBAIIi9izqpp0yTivfj75OCxW1hiNMBxiS+rFWgkEoYdYG/d86EK7Jiya2xUhN9Gb38fuJkOrZFWVC7GlHKg9SfA/OkznlUQ2MeAI6haRS2FjJZFgEBLwy8gff3V6GWS7l6UNyPHq/TIXuDIxUkcrL9IjWOGgL9euMpLiYyVk1TxcVVpK4vX8/I2mn4/NEcjVHQ26xDueg1EASif/3rc37+/nFhDE8OZ9nuCoKD5oJcw/DiHaTFZ/HUNDvRTz2B8+Ahyq69jvZdIZPR70l455yehPd0sHqsvH/ifeaumctjB59GP2UytnXrCHo8pAyOwm5x01zlOJe/kkv8Fy4VqeeQMb0jmTU4jje2llHW5IAJvwF1OMJXj/HQkAdpdDbyQeEHCAoFpttvw7lnD668PPqNi0ciE8jdUnPOr7F9z17KZ99I7W9+Q6vMxTM3S9g/xMu7ygwSZmykrDaSQVOSkKqkfHSwmmkvbuPu5Qdpc/l49tqBbP/dRH42PrVLpZn3TEhl/phkFu08yVvbykJGSlf+A+KHweq7uVyTyIyUGbxx9A1OWE5gmDkT1aAszJveQAyIHNv0/QH8b1KVH4qrMVnyWZdiRYuUKV7YoppMWVM7d43v3SXZYGfLqNQIxqZF8MbWUpyCCkbeQ1TxRq6IGcvHJR8THDMMg14gUqwnf0dtqPPcjaktbgMRzGEefFVVp6JnzAoj/e0W7GlXsaWwkWsGxSHrouyzC01smAqPMiQbV0rdoU6qw4fUY8WqBa3mx3f7uzOJJjVut5FWvYDt1gzSSldTdrSFQ2cwH/5TEYMiHlcQhdeBRBXAIo0kXNPzZjUTTRpa3JHIRJG69jqqbFV8WfElN8aMw1B7BHHUz1m8q5JMs54xvXtOEdMTyYgx4grEopBIcEZp0eDssk5qU2VITaFpKKQ6MbSZMqyuEJLHs7WoiREpJjSKnjPi0Cn5/axShhDdjzE+2BDIRWaOJjzYeE4cfmuK29AalQgHtiJPSmKzeAKASfXFYOpNe0QWnx6p4Yqs2NOa206O1JISqeWrUgckjmBYUzkAZYlyEEWMUitOm/dHN757Cv6gn335RxhYM5H0qEK+sLiYo2zGvnYdEXfeiTzuxwv7ruD20clUWpxsrfJB1o0Ixz/kjoybqG6vYd9wA8mrViI1hlF5x52UTJr8HQnv9DOS8P4nAsEAO2t28putv2HSykk8s/cZKttK+azsMzxTRhO023Fs2UpKViSCwCWX3wvIxbES7MY8ekVflDIJj3+Sh6gywmW/h5PbGG5tZkzcGN7OfRu7147xxhuRaLVYFi9BY1CQnm3mxO46vK5zI53zFBdTeffdVM6fj6eliU9mR3HHHBuxsSLLxz5H4tzV7NvuQ6GRUaAXueyFLfxq1VGkEoGXbxnCxocnMGdk0jnJcxMEgcev7MeVWbH8Ze2JUGaoXBWKuZFr4P05/CHrXowqI4/ueBRf0If5d4+gqC0iQW/j+NYaPP/l91aZb0EXaEWfauYj926mOxyoB9zA63ubSQhXM7Mbuco+PDWDZoeXZbsrYMTPQKHj9tYWXH4Xq8r+jXH2bGLyP8fR6qHyAs0xny7Vha3I5BI0ZfsBkI4ezq7aXUxGg6AM4xN7Br6AyHVDLx7nUplUgkQZQ7sSZL42HK0h4yS5qw2rViAsLP1CX+JZkRCuwRqIRC6KFA0yMWC4EXPjAfZ8XHbO5+o9Tj+iGIryUSiDCGFx3WJz6UzpFaGhVozC7A9Qb69iaf5SpIKUWxurQWXkQNh0CupszB+b3CPvryfRN1ZPgS+eXv4gzREyVB4LtuYu6qRW2TFog0iDPo5EOjDKdaQ4LLSaR1LU4Ogx86idfEvymzGdMU0VOPzteAamoa85hrXJhdPm7bLziaJIbVEbcb0NIdO9cePYVLmJzLBUEk/ugYGzWZNbT7s3wM3/xTDpu1yWGcXu0hZ8STlk1Oajl+vYYwx9dukspQAXTTd1b91eBhVORyq40fULQDDIyC+WIouLJeKOheftOqb1jyFar2Tp7vKQ5NfvZmLDSVLCUngn9x2U6emkrFxJ+G23ouzb9zsS3qfOSML7TSpsFbx86GWmfTSNezbcw67aXVyfOJkPpMl8WFaERBT53FCCNCoS62efotYriE0zcvLopSL1QnGpSD3HROtV/HpaJjtKmlmTWwfZCyAyA758jAeyfoHVY2Vp3lKkej3G2bOxrV2Lr6aGrIkJ+DwBTuzp2jgaX0MjtY89Rtk1s3AdOkz9vKnccbuVT1NaeC5g4IVbNhDWbxalJ1ooz21ht8zLn9YWEBOmYtH8bNY+mMPV56HTJZEI/P3GQYzpHcFvPzzGlsJGMMSFCtW2KsI+e4gnRz1OcWsxrx99Hc3QIeinTSNm5zt43QHytv3nLrTfG6C2qBVj3WFqhifj8ruYZbNSkHAj+8tbuWNcSrfq4g3rZWJCRhRvbi3FITVA9gIyCtYxJmow/zrxL7TXzyKy9TgqqY+87d1b8ltT2EpsuhHXju0oUlPZK5zEE/AwpbYI+lzBh0ea6BOjp19cz85H/S4aZS9a9CB1NtLe6sbd7kfZbsWlFgmLTrvQl3dWJIarqRMjifYHaLDXEvOHRxno2ILO08CX/zyOtencGSm5HF/nzSpUYArvmV3Gzhgac8DPiZZ8Pi75mKsSLiO6cD1kL+CdfY0YNXJmDb445rS7M5kxeoqC8aS4nVQavCjbarB1UVZqU6WdsGALglrNFlkpgxURCMA2bx+AHjOP+k1mDozlWLWVhtiJjHA5kSBQnKzAUBuaS+1Kya+1MVT0RiraEF0uAiOzONx4mMnScECEgbN5f38ladE6hvUKP+3jTsyMxuMPckw5BCkiQzTx7HHkokhNRVVyEAQumrnUbZuOEm9LZ7R+OR84M7m54RBCaTHm3/4Wifr8jUooZBJuGZHElsImymUp0GsskgPvsLD/fApbC9lZuxOJRkPMo4+S+OorZyzh/SbtvnZWF69m3tp5XLn6St45/g7p4en8bdSf2KwfyaNb/0m/qiOYs+aQ43KzuvTf6GfOxLF1G/7WVlIHR9FS035Ov8su8cN0n9X4Rcyto3rRP87AU5/lY/cBlz8DllL6l+3k8l6Xsyx/GS2uFkzzbgdBwLJsOdG9DJhTDBzbXN0lVu4BRzuNL71E6bRpWD/5FPUtN/DWHwbwQNxm+nkd/NswnJm3b6JZEsEL60/w1muHcQoiYrqeD+4axYf3jGZSH/N53clXyqS8edswMsx6fvHuIY5WtUHSSLjib1C6ifEnNnFt2rUsOr6Io01Hif7Vw+htFUTJWzi6sQq/7/v5obUlbQT8IhGWAv6dWEtyQGBQVBYv5WkIU8u5Mbv7yS9/OTWDVqePpbvKYfR9IJExzw3Nrma+dB0kbOIEYmt2UpHb3KVxCV2J0+bFUttOfKoO57596DoML4wyDUPsFuoSpnOkqo3rhl58C/FwYz8segG5vRGvO4AYFFG5bATUQSLM3e/9diYkmDTUdmSlNtirkOq0JD73DAOPvo7odrP2jdxzluPrsofyZmU+B8gVPdI0CUJFao0YiqEpdVTjDXiZ7/CCIKE24za+zK/nlhFJqBU912Crp9Cnw+E3xeenVNuOqr0Rt8N31mZg7VYPTqsXXXMxsr4ZnGyvZKjLBaberKuSEhumIj1a10V3cf6Y2RET9nFTDGGqCAZKtGyKbEDnqEIiiF1qnlRT1DGPWnUYQS5nT4wdEZFJdcUQN4SigJlDlW3cPDzxjNYpI1JMqOVSPmuOAYWObF+Acls5koF98B09iDFac1E4/NrtThT7EvBoyxmQ7mBboY0bj61BM3w4+mnTzvv1zBmZhEwisHxPRcivpa2SK/xyzBoz/8z951kdWxRF9tfv5w87/sDElRN5fNfjWNwWHhz6IF/O+pzX1X2Z9u+HURxcEmocPXAYrnqZ64Jqmv0OSkbFg8+Hff16UgaF/DEuufxeGC4VqecBqUTg6VkDaHJ4eHFDMaRPhd6TYOtz3Nf3NrwBL2/nvo08NhbDjBm0rVpFwGYja2IC1kYXlQWWn3xu0efD8q9/UXr55bS8/gb6SZOoe/v33Jb2FVvb9vNISytvDnyQmOsXs66wjfF/3czHG06S5JUwYFICi382kpGpERdMZqZXyVmycDgROgULluznZHM7DJsP2XfArpf5rSYDs8bMYzseIxAXhWnuXOIP/AunzUvhnu9Hs1TmW5CIAcKjgmwKFnCt1UJzn9tYn1/PraOS0HbD2JPBiUYm94nmrW1l2OQRMHguo/PWkWFIZVn+Mow330xM+UZEUey2Bko1haEFhslThej1osoZy7bqbUwUdMhUYbzf0huJANdchN2iiJi+WPUiqraGU4/JfQ4CKglxET27axxjUNEgRIakqs7Q35tm6BAS5l1PvyNvYKlxsGl5wTkxUurspPolDoSArsfFz3Ri1ChoU8YS4w8VQpPic0jJXQ39r2PJcS+CIHDbqF4X+Cr/N0iO0FIhSSTZ56PWBOqOGBr7WXZTT5kmlR7Ekhpa9A5tKCWQPI4dxc1MyIjqkVLuRJOGrIQw1hxvhIxpjLE2s41i5BHhGIXWLp1LrS1uQ21QIOzdgDp7GF817SBBYyaj9v+zd57hUdVpG/+d6X0y6ZPeAyQhQELvBEVBBMFeQMAurrrrFnfXVdd13VdXX11XXRv2AlZQmvTee0sI6b1Mksn0et4PEdd3dRUkkGJ+n3LlmjnzPynnnOf/3M99H4Wcq1i8pwq5VDhrZ3iVXMrotDDWFrUgJo4mr7EMgJpEHYHWVsLCJL2iSF3+wR4UPjX9Qv5FUeg4rj66CoXbQdQfft8lf3tRBhWXZEezZG8VzpQpoI9Bvud1bs66mX0N+zjYePCsj1lnr+Nfh/7F1E+nMn/1fNZVrmNq8lTeufQdls1Yyi2ScKIWTYM1D3U0PO7cDtOe7ohUlEgYm30T4f4AS9wbUaanYV26DEO4mvB4Xd9cahfRV6ReIAYnmLhuWAJvbi/neJ2to5vqsZG8731mps1kSdESau21hM2fR9DppHXxYlKHRKIxKDjyE+JoRFHEtnYtpdMvp+HPj6FMSSHyvTd4cZaKe4v+SoyjjSVNNm64/E2EUQt5YWMJd7y7j4woPQvDw1EbFEye0T2kiJF6FW/NGwbAnEW7aLS54ZK/QcIodMt/xZ8z51LeXs4/9v+D8DvvIDxYhzFo4cCayu+YCVUeasDYVsypwUakwHS/nBcas5FLJMztxvmD91+UgdXlY9HWMhh9L0LQzxzBQHFrMYcTRIzRBiJ8VZzYWkswcOYZYxeK6qJWFGoZyqNbETQajsT6sfvsFNSXIva7jI8PNjI6LZwoQ8/MDP0hImNT8WlF9O3/ntFU+Oz4ldIe2/07jVQiENDHkuXxUu228FnxZwBE3HUXsbEyUmtWc2pvIwfX/LCZ2U/Bbe/opDrlNgI+Y4/+WepCo4n1dVyr5ktCwWvHlX87H+6u5JLs6B5bgPc0ZFIJ6vAk4vwS6k0CKtfprNTOKVJ1bWUURgdQSuQMsLdSrsvD5vH3uHnUb3Na8tscM5FRtjZEAWzZCegbj9NUafteRdPZIooitcVtmONVeIuLkY8cxq66XRRITQgIePrN5NP91Vw8IJownfKsjz8hM5LqVhfNkSPp31SGWqriYESHvNMYtGBv9XTqfO2Fpr7UimV/gBMxWygIFrG6NIrpZdsJueoqVP36ddm65o5Kwub28/nhJhg6H0o3MMs0EKPSyOtHXz+jY7j9bpaXLufWr25lyidTeOHgC8ToYvjrmL+y/qr1PDLqEQb5AghvToWP5oJCCzd+Cjd8BJH//9zlQ+ZwucPJlsa9SC8twHXgAN6qKlIGRVBfZu01Blo9ib4i9QLymymZGNVyHlp6lGBE/45YkT2vcUfcFAQEXjz4Iqr+/dGOGknr2+8gCfrJGhdLxVELG94rZNsnp9j9ZRkH1lRydHMNRbvqKT3YRNWJlo6LUI2d9mYXLTv2U3rjzVQtvAekUuJefJHGp+7hurI/sbzkC+5oa+ddt5aU+etwJ07kl0sO8dTqImYMiuGZCZm0lNnIm5KIvBvJy1IidLxx81CabV5uXrQHm1+Aq98GTTgj1vyV61Jn8u6Jd9nnLCLi7ruIP/EZ1kYXZd/a/bK3emht8hLacoL3ok4xxulCN+A6PtjfxBWDY4nUd98CKTvWyJSsKF7fUoZVFQfZs5l6bB0RqjDePPE2puuuJbpwJQ6rl/Ij3c9AqbqolZj0EJxbOqJn1tZtQiNRMMLWQlHYZGraXMzuRYZJ3yY50oBPI6D0/Lur0NFJVXXLzv3ZogyN47p2OyNUUTy28zEONR1CkMuJeepJEqrXYg5WsuOzU1SdgyLk+zgt97UrnXgCph5dyMWH6ch36FgsT2Pgoc8gcTQf10XQ7vYzf3RSVy/vZ0Wm2UjQF0mTEZS+jr/ZczVPaqq0oVf7kQU8bDU2kKMIQw6sdqQhlQiM6sGRW9O+lvwutfUj2w96QcaxBDDUHSMYEGnsBNOh9mY39lYPYf4Oj45jaQr8QT8FtScheRxfVQq0On1nlI36fUz4eh54s38AcmCQOpoN8lNINBp0TSeBnjuXGgwE2fBeIQ6lFWPMemQRWcR+sRS/Uk3kffd26dryE030Nxt4e0c54pC5IFWgOfAON/S7gY1VGznVeup73yeKIoebDvPnHX9m0pJJ/G7L76hsr+SO3DtYOWslr095nemp09E4W+DT2+DVSWA5BZc9C7dvgbSC71+QPppZ4fkEENmU4QNBwLpsGSmDIkCE8sN9kt8LTV+RegEJ0Sh48NJ+7Kto5eN91TDxD6DQEr31Wa7tdy1flH5BSVsJofPm429qwrp8BdnjYgmL01F2sImjG6vZ82UZ2z85xab3i1j7xnFW/usIy547yCdP7uPDx3bzzh938MFbbazSz2XjhH+yLu0B3vzSw1f/U8qEHTfxq/23k1D/KOukr7D8MxePPLKF5u0NPBAXxbzwMPYuLUNrVJA17sJYkZ8NufEhvHTjEE422Lj9nX14VKFw7bvgbOa+4j0k6ON5aNtDKK68nBhNCxpfK/tXV3wjNaw60VG8GfVtnNBYmWlz8EFgMh5/kFvHdf/8wfsmZ2Dz+HltaymMuR+5z8H1qjh21O2gecJAwh2nUEk83c5Aqd3ior3JRXREEF91NZqxY9hQtYFxEj1KZQhv1SehUUi5OKtnZ4b+N8xGFT61AqXn3/NZCq+NoObs3Qm7IzGhBlox8Xd5ClGaKO7fcD+NzkaUyclE//Y3pG97FoPax1evHes0p1TokPtKA27atX7svoge3UlNCNVSGwij/6nNYK0iOOIu3txWxsA4I0MSztwEpo9zJzNaT6kvhkgRPIYAUvzYzjGGpqnKhtHfhCQsjN3BEga7PRDRj5XlIkMSQjCqe1500mlOS36XnbAiSxrLcG+A1aZqjO2lQOeYJ53OR9WV7kYWFcUq4RhhCgO5zeUw8GoW76kiNkTN6NSfVuzHmTRkROn4tNoA2gjyvH5OWk8hyx6AsmgXQI+V/B7eUE1LjYMtSR9xccspyiz9GdhwEs+N85GZuvbaIggCc0cmUlhvY3ejFLKugIPvc13yZahlahYdXfT/Xt/sauaNo28wc+lMblhxA1+UfMH4+PG8dvFrrJy9krsG3UWcPg48dlj/ODyfD8c+hzG/hHv2d8yfSr+7MVxpcfKLDw7w5y+Ok5h/G/kuNx82f4lm2FCsy5ZhMmswRKj7JL9dQF+ReoGZPSSOoUkmnlh5glbBCGN/BSdXcYu+H2qZmucPPI92zGiU6em0LFqEWi/n2j8OY/5TY7n9+Qnc+eJEbn12HDf/z2hueHQEV/9+KJffns7o8BNkn1hE/9LFDDbXM2JaPIkT9JSY91Gk24tGW0u2vxa9wYzbkEVdjYtjBxoJbwkwwqdAONrOto9P0VRpY+hlycjk3aeL+m0mZEby5JUD2V5i4VdLDhGMHgSX/xNNxQ4el5ipd9bz94PPEvXAr4gvXUVjhY2akx3FQcXeGhQeK0UZNkxBkTFRI/jnoQCT+0eSFqnv4jP7cfqbDUzLMbNoaxktunTInMpVhVtQS1W8U/0pIdMuwVy5icrjlk4tBs6VmqKOn7+p+TgAFVlhtLhbKGiswJ95GV8ebeLSbHOPygg8G2RSCX6tAWnQi1zWIcWW+xwIhu4TdXQuxJnUVAfD0LXX8/yk53H4HNy34T48AQ8h11xDyJgRDNj2FEG/n5UvH8Hn7RwjJZfNh9xnp10DrYEooo3dVwnxYySEaqgMhiME/XpOnuMAACAASURBVBCawhYhn5ImB/P6YmcuOJnReoqDcSR53TSZBNR+K+2Wn349ddm92Fs8aBtP4smMJ0CQvKYKXLGjOFJj7dFS39NMzTFzqNpKa/xkRlmbOaJqRhGiRCfYO2UutfZkGyqdHMnO1ajHjGJr7TYmSYxIpEqqoyez9VQz1wyNRyL56f8rEzMj2V3eii9xHPmNHQV2S2oYgcIjGMNVPbJItbW42fVFGZ7YZprDihhhd2FdXkiVIZrcuy9c5MwPMWNQLAaV7OuYvdvBayekcCWz02ezomwFFe0VrK1Yy8J1C5n80WSe2fcMeoWeh0c+zPqr1/PE2CcYbh6ORJBAMAD734Hnh8DmJ6HfVLhnL0x+GFTf9X+wunz8dcUJJj+ziWWHalm0rYxS43Bm+RVUeVqwjMvBV1GJ58gRUgZFUF3Y+oPxht2RimMdo28BX/cbAzsT+orUC4xEIvDYzGza3X6eXF0EI+6EkERM659g7oA5rKtcx5HmI4TOn4+nuBjH1q3feb9CJUNrVGIwShBWfoDz1lkoP3uJjAlpjH33MYY/dA0HkjbxR9/t7En8nOsi3ud+9ZNcfmsaMx+bRciMBJ6StPNprMglf8zn7hcncsc/J7Dg6bHMe3IMA8Z0vy7qt5k1JI7fXdqPLw/X8djy44g5V8Koexh08GPmhuXzSfEnHOqnJDnWi8JnY9/yEoJBkaqTbYS2nuCDmDKm2WzsMM2kxeHl1rEpXX1KZ8x9k9Nx+gK8srkUxvwSo6uNGboUlpcuR5g1FXPVJoRuZqBUU9SKWi9HumcditRU1noPIRekjG1vZa92AjaPv1e6+n4bMaTjf0ohcSHFi1vuR2fsHWY4caFq6sQwAi0VpJnSeGLsExxpPsKj2x8FwPz4X9BJXeTUL6O52s7Gdws7xUjJ3e5G4bXh04j41DEoZD33dnY6hgaA4Xfyxo5KwnXKb9xT+7hw9Is2UCzGkuz1U673oLI3nNOm3+niRl1xkKp4FRIEch1WDstzABifEdkp6+5KTkt+V3gHMsrlBkHA0i8aQ8tJ6kus5/z/XlPcRlS4iGizUT0gHKffSUF9CWRM4cPDViQCXJl3buMiEzIj8QVEijSDyW6rRyGRczw6AH4/Jr2/RxapW5cUIwZFVsW9xQRUOMrj0LS1cGDGPFTq7rGpp1ZIuWZoPKuO1VOvz4KYIbD7FeYOmIMgCMz4fAb3b7yf45bjzM2ay9KZS3l36rtcmXElesW3mgulm+Dl8bBsIYQkwII1cOWijq//A18gyNs7ypnw1AZe3VLKjEExfHnPGORSgbd3VXNR/2vRB4J8FlmCoFRiXdoh+Q0GRCqO9hzJryiK7F5WytHNNQjSnrnZ2XPv6j2YftEG5o9O4sM9leyvdcJFf4bGY8zxSAlVhfKP/f/AOG0qsshILIsWfef9YiBA26efUXLJpTT97/+iGTaMlGVLMT/6CPUqDzevupnn9j9HgSGTz8qKGe2XwILViFmzeHlTCbe9s5e0SB1LF44mO9YIgFQmQaWVozEoesTO/e3jUpg/Opk3tpXz8uZSKHgEUiZy976lpGljeHjHIxh+eSdxVeupPtnOiW21eP1StPJ6ao1BZqLjz4Wx5MaHMCw5tKtP54xJj9JzeW4Mb20vp9k0EJLGcmPZIfxBP59IDhCSmUC4q5QT2+oIdAMDJVEUqS5qJTbVgGvPHnTjxrKuYh0jJTq0KhOv18RhNqoYkdIzMy7PFF1oGk4lyPxtSIMOrFoIC+kexmTnSrxJw8FgKor2ctj6LJMSJnH3oLv5ovQL3jn+DrLwcMx/eQzjoVVkhdZycncDh9efvRncf+JscyP32kEVRBLSs+eZE8M0bAwOojqqgNK4GWwsauLGEQkoZd1T0dKbiTIoqVMkk+zzURUSQGVvpL3Z9ZMLrdPFjd5exb5wGxmKEHSiyLLWZMJ1CrJ6QS50fKiGnFgjS4oFYsMGkCTKOBTnx9BwHLfDR1vDT8+YtLW4sVncmJwVIJXyVWQDeqmaYa31BLKu5KN9VYzPiDjnmfT8JBM6pYzl9gyUIuQoI9hk7JiBNfoasVncuB2+c/qMC0n54WZKDzYROVagTlLJpWWVNB0U2G7OJnv6RV29vP/HjSMSCYoi7++qgOG3g6WY6IZCFg5ayMWJF/NCwQt8deVX3J93PynG/2gqNJ+CD66Dty8Ht7WjMF2wBuKHfedzRFFkfWEDlzy7mT8tPUZmtJ4vFo7hqatyyY41Mn1gDB/trcKXfSNTHQ5WtGxHOX4M7StWEBWnRm1Q9KgomtriNhorbGQPUp+TyqAr6StSu4h7J2cQpVfx0OdH8WdOh4SRaDc/xa39b2JX/S52Nu8jdM5NOHfsxH38+Dfvs2/ZStms2dT9/vfIIiJIePst4l98AUVKCh+d/IjZX8ymxFrC30xDeerASozRg+HWDXgisvn1x4d5YmUhU3PMLL5tZI92UhUEgT9O68/03Bj+trKQTw7Ww5WLUBpj+Ut1Oa3uFp52fE6/gVqkfjdbPiwCoCj2JAM8HgTzlZS1eLh9XEqPKMq/zS8K0vH4A/xrYwmMuZ9Eax0TDKksLlqM7prZmE99hbPd2y2G/NsanDjaPITLLB1xSIOSqHXUMrmpBlfaNNYXtzJjUCzSHnoBPVOiw7Ow6CGsbQPRLauwasEc3b+rl9UpxJk0LApcSpn5Ulj7MOx+ldsG3sZFiRfx9L6n2V67Hf2kSYRcfTWRnz5BfIKUbZ+c+iaW6KfisnuR++wIKhGjqWdLJs1GFUVCMh+k/JU39zahkEq4YXjv6LT3NARBQB+dQpxPoN4EKnczPk8Qj+OnyfyaKu1oFV7kfhfrNRUM9voRo7JYWeZnXHpEj314/E+m5pg5VNWGNaGAkbY2VpmqvplLPRfJb+3X+ai6wq2ocnNZY9nOWEGHXGlkM4NpaPdw7bDvdsvOFrlUwtj0cD4rlSGaksn3+NjnLUYWH4emruMZrKd0U33eAJs/PElojJYD5nXopEqSd0gIBuCt3Mu/MYrqLiSGaZmYGcn7u6vwZl4OmnDY/QoLchbw5PgnGRc3DpnkP8aBnC2w8rfw4nAo2wIFD8PCPZA9G77nme5EXTs3vb6b+W/uJSjCq3Py+eDWEd80agDmjU7G4Q2wpBhmm3LwEuRYfhiBtjYc27aRnBtO5TFLpzhWXwgOrKlEgQfl/9xF0NV9RsDOhr4itYvQKWX8afoAjtW28+6uSpjyODiauLqhArPWzHP7n8N41VVINBosi97AfeIElfMXUHXrrQQdDmKfeZqkJYvRDhtGs6uZhesX8ucdfyY3LItPA5FM2/8JQt48mLMUCwZufG0XH++r5r7J6fzzusG9IhheIhH4+1UDGZ0Wxm8+OcyGKj9c+wFZDhu3+ZQsL11O2TW5xDZsJxAAva2SpcnVzLS7+WvDUBJCNUzJ6nlzgakROq4YHMc7OytojBgF0QOZU1dOm6eNjek+IvzVqHF2CwOl04WIoWIPgkbDOlMdEgTG29rYJBtNICj2eqkvQFzcYFr1oG84SFj9LpxqiDKndvWyOoVIvRKZVMaS2D9A5lRY8QCSQ4v5y+i/kBaSxq83/ZrK9kqifvsbFAlxpK9/AmOYktWvHcXW8tMMaURRxO0KovDZEZVSYk2aTj6rC4tMKiE2RM3RmnY+3lfNZblmIvRnH6XRR+eQaTYiesOoNwnfZKX+1LnUpiobBm8jYmIsFpmbIc3VNEeMoMXhZXw3KxbOhdOS3/XiEEY7nZQbPKg0HuR4z6lIrSluQ6GSIj+8BevgFNo8bRQ0lMGA6by/v5FwnZJJ/TpHMj0xM5L6djdt0aPIaywjKAZxZMSiONYxdtVTitS9y8uxtbgZfU0q66vXcXWNEnu5hlUDJpExuF+3dJWfMzKRZruHlYWtkDcXilZCa8V3X+j3wo4X4R+DYfcrMPgm+MV+GPtLkH+38dJoc/O7Tw4z7R9bOFJj5eHpA1h93zguGhD1nQZFTpyRvEQTb20vJ2PIbfT3eHlLsR2pyYT1iw7Jr88ToLrw3DZYLwQttQ4qjliIKV/LtiwnHrGvSO3jLLk0O5pxGRE8/dVJGvVZMPBaFDtf5s70qzlmOcaGtt2EXH017StWUDZrNu5jx4h68HekrFiOYepUBEFgbcVarlh6BbvqdvG7AbfwcvFhoku3wLRnYPqzFDV7mPHCNg5XW3n+usHcNzmjx3UOfwilTMq/bsyjX7Seu97dz0FvDMx6mVuqCukv0fFIyQtkjDQiCXjRBktoM8KIsNFsrBa4ZWxyj+3g/aIgDX9Q5MVNHU6/eY2lZGliebvkQ0JmzSS6YiNVx1uwNnXtham6qBWdSQnb16AdOZK1tRvJk+gIVYXyYoWZ7FgDGVHd37TqXEmMS8CpA6XVg8rux6uBuPDe4e4rkQjEmtRUWn1w5RuQPB6W3oWmeA3PTXwOiSDhnvX34JSLxD75JNRVk+dcg98XZNXLR37SrrTPEyAYFJD77Pjkqh4dP3OaxDANm0424fQGmD+6+7uN92Yyo/U0+WKxG0RU7tMxNGe/oeJx+mhvcqGtL8SS3OGkOthpZ1dwAIIAY3pw9Mx/khCmITvWwFvloQyVGpEJEhoywjDayqg/1fbjB/gv1J5sI8LgQUBkR4IbhSBjjK2V1tQZrC9s5Mq8OOTSznmUPb1psFvIJtfeikyQUhovR1pfhT5E3iOKVEutnYNrKuk3ykyF9gQ2j5WCL5uR6BS8mjiBi7vpxvy49AiSwjQdBkr580GQwJ7X/v0CUYQTX3Z0Tlc/CLFD4I5tMP1Z0H13k8LtC/DP9cVMfGojH++rZt7oZDb9egLzRif/oH/BvNFJVLY42RgczGyvQKGnHv+kEdjXrcccI0OhkvYIl98DayuREiC6bhPFgwXU6p7pEt9XpHYhgiDw6OVZePxBHl9xAgr+BIKE6UVbSDGm8PyB5zHMuQFlWhphC+aTuuYrQufORaJQYPPa+MPWP3D/xvuJ0cWwJOd+blj7NBJXK8xZBkMXsKGwkdkvbcfrD7Lk9pFMz+3ehkg/Fb1KzhvzhhKuVzD/zT2Uhk9EPv63/LWyGLvXyqu55YxpfJNdsSspcDj5yH0RJo2cq/J+WqZadyAxTMuVQ+J4f1cldbEXI5iSmWO1Ut5ezqkJqcTUbEVA5PjWmi5boxgUqSlqwxwrw19Tgzu/PyXWEgpa6mhLupTDtQ5mDe7Zs4RnijlEjUcjoLEHUbtFvCoJBnX3283+qcSZ1FS3Ojt2sq/7AOKGwccLiKs7ztPjn6aivYIHtz6IcmAO4Xfdibj8Q0bnuGissLHp/aKznvdz2ztmwxQ+Ox6FvkfHz5wmPrSjGzw0yfT/JGh9XHj6Res5FYwnPujDp+ooTH6KeVJTlR0AbUMhhVEBYqVaooIiS5oSGBhrJEzXu7rlU3PMHKxuR0wsYJDHxx6zE0PTCdoaXbhs3rM+nqPNg7XJRYj1FNLQUD4XDjJK0KDRRvFBYyKBoMg1QzvvPh5lUJEVY2BJcwoaUWSAwsQOU0cn3aR2d/siVQyKbHq/CLlayqhZqawuX82UYzLkTRKqLirAp1AxuX/3jHqTSARuGpnEvopWjtr10G8a7H8bvE6oPQhvTYfFN4BEDjd8DDd+ClEDvnOcYFDk8wM1TPr7Rv7+1UnGpIez5pfjeeiyAYRoFD+6jilZ0UQbVLyxs4ZLM2ajCgZZn+5A9Hpxrl9LYk445YebCQbP3fzvfGFv9XByVz1RtVvZMsDJ7PwF3yuB7gn0FaldTHK4ljsmpLL0YC3bm5Qw+l5kxz/nnvgplFnLWOXYQ8qypUQ+8ABSQ4fBwp76PcxeNpvlpcu5Y+DtvBsygpTPv3Y0u20jYuIoXttSyoK39pAUrmHpwtHkxveOrs1/I1Kv4u35wxGAOYt20zjkPtJSLuYeSyurmzfz5v2xrMzxME0I5aXySG4akdjjJc8LJ6UhIvLPjWUw+l4uqj5OlCKEN9pWEjosh/D2Ik5sryPg7xoDJUutHbfDR6izEoAdiR4ACtqtrAiOQCoRuHxQ79w4+U9kUglereKbC65f3TMMys6UhFANxY12TjXaQKGFG5Z0PEAsuYlhbg+/GfobNlZt5MWDLxJ+++2oc3ORvfwwQ8aHU7ijnqObzm4zxWXrKFL92PEGQ3pFJzXh6yJ1Xl8XtcvJiNJ3OPz6/DQZXMiDbtotZ99J/bZp0mZjPUN8Iv7IHLZW+3pF9Mx/clryu02Sz2iHnXXhjYRYv85LLT17yW/N1/mo2iPr8Q/NptZVT0FTFWLWLBbvq2VESijJ4drOOwE6JL8bq4MEInPIc3vZoCxFUKnQu2qxNrm6dQRJ4c466k5ZGTUrDZlaYPupdVy33oc63M//hk1hWFIoodofL9S6iivz4lDLpby9o7zDQMnd1lGcvjIBGo/DtKfhzu2QftH3Fl17y1u44qXt3Lf4IKE6BR/eNoKXb8o/q78RuVTCTSMT2XqqGUvStVzkcPGesBtZYgLWpctIzg3HZfN1Sv7v+eLwhiqCgSBxVesoGSLhrrXpuDop+u1C01ekdgPumpBKQqiGPy49inf4QtCbKdj/EdlhWbx46EU8gY6He0/Aw1N7nmLB6gUopArevuhV7i49hHztI9D/cliwGq8ujgc/PcJflp9gSlY0S24fidnY8x/gzoTkcC1vzBtKi8PL3Df30T71n8yRRzHIG+Criq+I8vtpkV+GQiplzqikrl7uORMfquHq/HiW7K2iKmEGcl0UN3ol7K7fjeOyMcSUr8Nl83WZNOX03Ia+cBPK9DRWufaSLWiIUoXyQmkk4zMiCO9lnYQfImD4t4tnQNu5D1ZdzYIxyWiVMq59ZScnG2ygMsKNn4EpCT64lut06cxKn8XLh1/mq+p1xDz5P4h+P+bV/0tidihblxRTexaSQJe9oyvjltlx+sN7RSd1xqAYHrg4g4sHdM9Ox88JvUpOuy6VJJ+PCqMPlduC7ad0UittqKVeFIKHIyHtDGmtpcKQR1CkV82jniYxTEt2rIHX65IY6QlQbwK1vBWB4E+aS6092YZcLqCpO8HRVDlSBCbYbRwOu5gKi5Nrh567YdJ/MiEzgkBQpMKYT35TOR4hgC8jAU3VEQCau2k31W33sf2TEsxpRvqPNLOjbgcXb7CidgTRTx/AsWY/U7K697XFqJYzc3AsSw/W0ho+FKJyoP4wjP4F/OIADL0FpN9VIFVanNz93n6u/NcO6q0unr4ql2V3j/nJqQHXDUtAKZPwylGYpU/Djp/6MRk4d+8mJsyLVCbptpJfr8vPsU3VRDQfZH9KC3bvKKbkxPTYpkxfkdoNUMmlPDoji9ImB6/uaoCCPyHU7Ode02DqHfUsLlzMCcsJrv3yWt4+/jZXZ17NknHPMfCLX8ORj2DSQ3DVm7T65Nz0+i4+3FPFPZPSeOH6IWgUvUdSeCYMjAvhpRvzKG6wcfvikwSufo/H25xogkFmO7w8VjGQ2XlxvaY4WjgpDUEQeH5zFYy4i1kVh9BIVbxrKiRS1YZatHeZgVJNUSvGcBXins0wIo8jzUcoaG2kPnYKNe0+rhjc+w2Tvo0k9N+Zl4Kh58QenQkpETo+vG0EEkHguld2UljfDtowuOlz0EYgvDebPyTNYFDEIB7a9hBlejfRv38Q186d5KkOoQ9TseqVo9hbPWf0eaflvk6FDS/mXiGdNhvVLJyUjqyT5uv6ODcM5lTivSL1JgGVo5H2JsdZH6O5yobBU4crOZqAVGCIy8lGbz8MKhm5cb1T3TQ1x8yuajdJ0cMxiQK1KRoMzpqfVqQWtxGusiFB5PPQMvJEBSGmFBaVGDGoZFyS3fnzlYPiQzCq5azzDGCw04EEgdpEHaoT2wFo7KZF6vbPTuF1+Rl/XSaCRGD7zo+ZtkdEn+xkf1IBQLedR/02c0Ym4vEHWbKvGm76DO493BHTqPruCITV5eOJFSeY/Mwm1hc2cv/kDDY8MIHZeXHn5JodqlUwc1Asnx2opl/2AhJ9Pj6KOQWAa80q4vqbKD3Y1Cl5353NsS21eD1BEiu+4ki+wHbHZdw1oeeaNPbdDbsJEzMjuSQrmufXF1MVfzmYBzFi11sMjxrKi4de5PoV12P1WHlp8kv8MeYiNIsu6ciHuu4DGPcAp5rszHxxGweq2nju2kH86uLMXmNtf7aMz4jgqasGsqPUwv1r7cTNeJU1VbUMU0ygLaDkljG9R05nNqq5YXgCn+yvoSLlWgxyA7PQs6JiFYqZl2IuW09NUes55dT9FIKBYEcAu8GN6PNxNE0OQIG9nU89w9ArZVz0M+sYaWMyvvlaGd77CvTUCB2Lbx+JXCrh+ld3cby2HQxmmLsMFDoU717F/w5ciF6h5xfrf0Fw2iR0kwuwPvc0BZcY8HkCrHrlCAHfj8vTT8t9HUoHMk1Sr5JO99E9yDCHIPWaOrqBLgu2Fg/iWcyhed1+WhucaOoLqYxTEiJRkOQXebc2hrHpEb12M+K05He/ciQjnA62RbZisBTRVN5+Rv/bp3G2e2mtd2JsOoHQL40jgUomtdTj6jeblccauGJwLCp553eHZFIJ4zIieKsmBp0gI1Nu5ECEA7mzFa1W0i3nUmtPtXFiWx25k+MJi9XhDXhJfGsjolwgepCTNxoyyI0z9oixiP5mA8OSQ3lnZwUBTXjHPeQ/8AeCvLOjnIl/38grW0qZMSiGjb+ewL2T0zutMXPz6CTcviDvtw9klltkg6waYeAArMuWkpwbjs3iprna3imf1VkE/EEOravA2F5MUXQVHvK4Ii+JuB7sft87r5I9lD9NH4BEEHj0y0K45Alor+F+SQSegIdJ8ZP49PJPGdNQBm9d1jH3dctayLyUTSebuOKF7Tg8ARbfNoIZg3rfA/DZcsXgOH4/tR/LD9fx58IY5Lds5Y6GWVzUP4qUCF1XL69TuXNCKnKpwLNb6mHoAm4oP0RQDLIiy4u5eQ8CQY5tvbDd1MZKGz53gBDLcSQaDV/oTpEiKElShPFSWQRTc8zn5QGjOxOblI/z63EgQ1xm1y7mPJEcruXD20aglEm4/rWdHK2xdszKz1kGgkD44pv5R/7vsLgtPLD5ASIeeQiJ0Yjrf37PpOvTaChrZ/OSkz/6OS67FyHow6N2Iw/pfMlfH31kRutp98bRENKRlRoIdBROZ0pztR1E0LeWsje8nUF+AU9kLmU2Sa+cRz1NYpiWrBgDbzRlMtrlZnesmxBrKYGAeFZdyNriDvm/9sRmKgd0yDYLHE5WiKPw+oOdko3635iYGUG1Q8AZOZg8l4u1+g5fhRCFg+aq7lWkikGRzR+cRBeqZOi0jg34fUtfY1CxD+9QKYH0kWyvDTDlPHSdzxdzRyZR3epiQ2Hj//u+KIpsKGzkkue28NDSY2RE6fhi4RieuiqXKMN342fOhf5mAyNSQnlrVx2XJV+GTBQ5MkiL91QJMapWBIFuJ/kt3tuAw+ojsXwNe4fCVtsV3DUhrauXdU70FandiJgQNfcWpLP2RANrHKnQ/3Ky9rzNtmmf8PTYvxGy4QlYthASR8Ot6xEjMnlzWxnz3thNXGiHQdLghJ5pM30+uHVsCgvGJPPm9nJu+LyNJhfcPj6lq5fV6UTqVcwdmcTnB2soTb2JOFFKgTyMdxu/xDR+KBGtxyjcXntWu9jnyul8VM3+1ciH57PHcoCCthZKIwuwe38e2aj/Sf/U4bTowS2H2ITBXb2c80ZSuJbFt41Eq5Bxw2u7OFJthfC0Dumvz0nWsgd4eNC97Knfw9OnXiPmr4/jOXkSw8b3GDIlkeNbajm25YeNlFxWNwqfnaA6iD605zx89dFz6BdtoDwQh1QX/HdW6lnMpX5jmmSrYmeIhby2RgqVuQCM68VFKnRIftfXysjXJnd0oiUNANSVnPncee3JVmRSEX17OWvNFrKCUqKicnnlmITcOCP9zYYfP8hPZFxGBIIARxSDyLfU0KDxIUaFo7dX0trgxOvuPuZJJQeasNTYGTkzFblSiuj1IvzjDepDJQw0V3JAOwagR2XCX5wVRZRByVs7yr/5XmF9O3MW7Wbem3sIBEVenZPPB7eOOK9O6PNGJ1PT5qIwZBbjnC4WRRwHmQzvmi+JTjVSdrD5vH322SKKIgdWV6Bx1VOrP45Pmc20wSkkhPXcLir0FandjvljkkmP1PHIsmO4JzwCQT+atY/Cu7Ng179g5EK44WN8yhD++PlRHvniOAX9o/j4jpG9wjykMxEEgT9M7c+MQTHsr2wjL9FEXmLvmgU8ze3jU9HIpTy9vQ0G38icyhPYvDYOjYkmpnIjboefkoONP36gTqK6sBVTuBxJ1SnKB4QSEAMU2Nt535FHbIiaoUm98/fwQ8RFRmLTgVUD6Qk5Xb2c80pCmIYPbxuBTinjhtd2cqiqDaKzO2IDHM1M3/g8N2dcw4dFH7IquhHT9dfR8sYbZEc2ED8glM0fnvxBN1BniwO5105AKcFs6l0mVH10D1IitJQK8cThwy/r2HQ7G4ff5kobSokHmdJHgwkGu12sdKTTL1pPtLFzuz7djdOS3wbNaNJ8PqrifGg8lrNyRK0pbiNU0opUq2a1tpTJbRZq4qdT1GDjmvNgmPRtwnVKBsaFsLQ9nSHujt95c0oo6vIDINJtZJ5iUGTvijJCojSk5XeMzzS98zbGejuF02JQSOHtlgGkR+pI7UEKMrlUwg3DE9lS3MyuUgsPfnqYqc9t4XC1lYenD2D1feO4aEDUeR/zmNw/ijiTmn8ekTBbnUC1woNr2ACsK5aTMjAMS429y7PoT1N5rIWWOieJ5V+xc5jIlrarWDixZ3dRoa9I7XbIpRL+MjObmjYXzx/0wfA74PjnULkLZv4LpjxOmyfI3EW7eW9XJXdOSOXlG/PQKnu+ccj5QCIReOrKXO6emMqjl2d19XLOG6FaBfPHJLP8SB3F/P72wQAAIABJREFUafMY5HEzUG7iX2wiKjyAOmDl2OYLI/kN+DqcHCOkHd2Hr8xNmJGTKQ/lzaooZg2J/VnOS8ukEnblyVgxWiAqtPcrHuJDNSy+fQRGjZwbX9/FgcpWiMuD6xdDWwX3HVrJ6OjhPL7rcepuvhhFcjL1v/89BVfFozMpWfXyERzW7zdScrV7kPvs+FQy4kx9m3N9dD5yqQSvKYNknw+L+id0UqtsGJy1NCebUApS+vtEPqiP6dVS39MkhWsZYDbwXtsARrvcbIuyYmgtpq645YzMZlx2Ly21Dgw1B2kdmEBQIjDJ5eZN62DUcinTc787p9jZTMyM4OOGKEKkatKkWk5EB9BUHAKgqaJ7SH5LDzZhqXGQPzUJiUTAb7HQ9MILHEgRyIlw4DfnsbpK0qO6qKe5dlg8cqnANa/s5KO91cwbncymX09g3uhkFLILU7pIJQJzRyaxu7yFhKQbifT7WZ9qJdDUTHSgCug+kt8DX5Wj8FmxS/bi06dz8aB0kjo5nqkr6CtSuyHDU8KYNSSWVzaXUtr/TsifD/NWwKDrKGmyc8WL29lb3sozV+fy20v6/Swf+M8GhUzCr6f0O6+ykO7ALWNS0KtkPLnbA1mzmFNfSZW9mqYpg4kp30BtcRut9WfvUHm21JdZCfiCGKr3IU9LZY3rAAXtbZwwTSIgSn52rr7fxp+QTltq+M/G6CfOpGHxbSMJ1SqY8/pu9lW0QtIYuOY9pI1F/E9VGbFaM/fv+B3Kx36L32Kh7anHueT2HDwuP6tfPfq9Ob8uuw+Fz45Hoe4RZiB99EwMMWnE+4LUhfhQ+NrPuJPq9wZoqXOgbSiiMMpPTlCCIzQXW0DxsyhSAaYNNPNJXTjDRTVH4kWM1lLcziDWxh8v9OuKOzquhpqD7ErwkhyAhJhRvH/cw2UDzehV8vO9fCZmRuITZTSE5pHncrEppA6ltx21SqSpG8ylikGRPcvLCYnSkD706y7qs8+Cx8Mnl+gYXnuM48axBIJijyxSI/Uq7p6YxvTcGNb8cjwPXTaAEM2Fz3i9emg8armUfzXmMNPl54PoagS9jsCGLwiP11HWDYrUxop2ak5aia9cz6bhAba2Xsvdk3p+FxX6itRuy4OX9kctl/LHVZWI056BuHy2FjdzxQvbaHf5eP/W4cwaEtfVy+yjG2HUyLl1bAprjjdwMn0BBe2txMp0LIotIdZ6sMNA6QJ0U6uLOkwF1Pu+onlQAt6glwKHnTfaBjEoPqTXGVedDQ9c9x6/uWpFVy/jghIToubD20YQrlcy5/Vd7C1vgfTJcOUijDUH+IctgNvv5lf1L2K66w7aV6xEcXAjk27qT90pK9s+Kv7OMd3uIHKfHYfC0Fek9nHeyDCHIPeEUG8SUDubaG84M5lnc40dMQh6WyU7TRaGtLdwUJqDRiElL6n3qyjgtORXwKAeQYtJRC12zJmfyVxqTXErUiGIob2CZeGVFNis7DMU4PQGzqth0rfJiTUSplWwQ8whv62RE+EeRLkMo8TaLRx+yw41Y6mxf9NFdR09RtvHn7BmqIKshGTkwBLbQGJD1GTHnr/53fPJfZMzeP66wSR3YUfQqJYzOy+WTw9buDRmMl6ZQE1+HLY1a0keYKSu1HpWhmrngwNfVSINuhE9W/GFJzA+t3+Pknf/EH1FajclQq/k15f0Y3uJhS8O1/HOzgrmvrGbmBA1n989mvyf4UxfHz/OvNFJhGjkPL5PhiztIm5obWFn+yEkkwYT0XyIwh21+L2B87qGmqJWwkwicnc72xPchCIlW2ris+bYn6Vh0rcxG9WkRfaOm8fZYDZ2FKpRBhVzFu1md1kLDLgcZr5IStlO/iaGcsJygmcHVKAeMoT6R/9MUoyfQZPjObKphhPb/725EvAF8QekyH122uXhROl7R+ZxH92PftF63F4zdaGgcltobzwzJUrz10WMzlbFSbPIELebT9tSGJUahlL283A1Py35/dIxkDy3h4qIRmQB1xnlpdYWtxHib8SXEEaTXqTA7ee52v6kR+oYknBh8mUlEoHxmRG835RMntuNXybgSIpE11ZGa50D33m+j/4QYlBk9/IyjJFq0vMjEUWRhr/+lYBBywcj/UyxthAMS2dJhYaLs87/7GZv5+ZRSXgDQXbLLme4y81HCdWILheRjmIQoexQ13VTrU0uSvY3Elu9hY35bra3XN8rZlFP01ekdmOuH5bAwDgjv/7oEA99fpQJGRF8fOco4kN7tltXH+cPvUrOHeNT2XSyiaL0BVxhaUAnUbBsoIeY6s14XAFK9p8/AyWfJ0BDaTuh7ioErZZPVEeZaLdxSDsemVTKZQNjzttn99G9iTKo+PC2EZiNKuYu2s2OEgvkXgvTnmHCqW3cI49hecVKti/IB1Gk9ncPMuLyZGIzTWx6/yQN5e0AuB0dGakE7YjK2F6bN9lH15MZbaDWl4DNIKJ2WXDYAgQCP+6S3lRpQ4EH0ejHoRHI8cFX1vifjdT3NNMGmnm7IZkR3gC7Y90Y20qoK/xhR1S3w0dztR1DzQFOpKmICgRJiB7P9mov1wyNv6AF18TMSPa6ogmRm0gUVJTESdGUHUAUwdKF5kllh5uxVH/dRZVKaF++Atf+/eyakYbcYGRYxX5Kwybg9Qe5pAdKfbsbaZF6xqaH8+wROVfIo9hudhGIDkfY8DmGcBWlXejye2hdJQQDqNs34I2JZnh2DulR+i5bT2fTd3fvxkglAn+ZmY1MInDbuBRemZOPrs8gqY8fYc7IRMJ1Ch49FIIudihXOrws9u8kPFmJxtvC0R+J9zgX6k61EQyK6Iu24B6cTrvoosBu52VLLhMzIwnVXviZkj66D5EGFR/eNpI4k5p5b+5m+6lmGLoALnqMW07u4BJ5BH+rfoP2u6/GuWcPbW+/yZRbs9AYFKx6+QjOdi8ue4e0yiu1oVQndvEZ9dGbiTGqqJEnotH5UbktiAjYW77fzOvbNFba0DtqqIxTkBmUYDcMxIOC8RmRF2DV3YepOWY8KEiRZ3A8QcBoLaXN4vv3RtP3UFdiBRFCWopYGVXHJLuDFZKxKKSSCz7iNC49AkGQUKLLI8/pYEdoC7qWEoAuk/yKosie5WUYI9RkDI0i6HTS+Pe/oxjQn9cSSinQpSAP+vncPYQwraJPdddJzB+dTKPNQ3jYLAzBIIdzlDh37CApQ0t1UQte14WPJXLZvRzfUkNUwx42Dm5nd8t13NNLZlFP01ekdnMGxoVw6OGL+f3U/kj7DJL6OAM0Chl3Tkhje2kLJ9IWcH1TDYhB9o0MJ6ZyI/Ul7Vhqz88ucHVRKxIJ6Mr2cChVihYJuYKJDY6En73Ut48OIvRKPrhtBImhWua9uYetxc0w+hcI43/Lo8X7yZTpuFf5GZJJY2h87h8Ilae49I4cXHYfq189isPaUaS6ZQ50xvQuPps+ejOCIBAMyyQWHwGhBYB2yw8b/wR8QVpqHWgtp9gfZmOwzcrOwACSw7U9PrPwbEkO19LfbOCoaygBQwClvxLgB6Noak+2IiGIzlPLkdgABT6Bp0sTuTgr6oJvcho1cvISTXzlyiTf3srhKDdKTxtKWaDLitSyQ800V/27i2p5fRH++npqF1yCPeBkiq0dUW/mrQoTk/tH9T03dhLjMyJIDtfyTHUu010+3ktugGCQiLZjBP0iFUctF3xNRzfVEAiAqWktzsRQBg7Io190z5w//m/0Fak9gD45Wx9nyw3DE4gyKPnT8TiiQ9O52C/lpZD9xHiKkBDg2JbzY6BUXdhKmNaNNOjjk9ASxjkc7FOOxaBWMrHfz6uL0Md/J1yn5P1bh5McrmXBW3vYfLIJJjyIZsTdPFdWiCzo5U+japCYQqj5zW8Ii5Qz8YZMaovbvjFS8ils6CPiu/hM+ujthMSmk+AL0qrqkPTZmn/Y4belzkEwIKK3VXE82s9gj4ePWpJ/dlLf00zLiebNpn6MdLupMJUhiAFqf8A8qaa4DaOrmvp0HTpBJCJ0Ak0uuPY8Z6P+NyZkRrKkJZU8t4dmA/hMOoxiC41dUKSe7qIaItRkDIvCV1eH5fXX0V96CV/qSwhRGhlatpua6EnYPEEuye6T+nYWEonA3JGJ7Kp2MzFkJJVh4EiNQr7xE9QGxQWPovF7AxxeW06Y5Shbs+s50Ho1C3tZFxX6itQ++uiVqORSFk5KZ0+llaLUBcxpqKZNdNA0LoGIxv0Uba/tdOMHt8NHU5UNU0shgZR4ylR2Cux2XmrO4bKB5p+NYUgfZ0aYTsn7t44gNULHLW/vZcPJJrj4L8Tk3sQzNVWcDFTz2dWxeE+V0Pj0M2SOMDNwYhyt9U4AfAo7ZlPvmb3po3uSYTah8hho1rchiIEf7aSe7rBpndWURUGuT2CvL5nxmT/PInVqjpkmTGQTzqF4L3pbFXXHGr73tV6Xn+ZKG8aGY2yKaWO808kHjuHEmdSMSg27wCvvYGJmJNViJAaFmRhBQU2iFq3lFK21Dvy+C2ueVH7E0tFFvbSji9r49DMQDGK87x42Vm2kwJiJ3OdklS8PnVLGqLSu+Zn1Vq7Mj0enlLHJN40ct4f16Q68x4+TkCin4qiFgO/H59U7i8Kd9bhdIpH1a2lP05OWOYqsmN4Xs9hXpPbRRy/lmvx4YkPU/L44gyxVJENEBa8mVxBTtw2vJ8ipvZ1roFRb3AYi6Ao3U9rfiAKBIYSw25fSF5fUx/cSqlXw/q3DyYjScfvb+1hX2AjTniE//XIebLbwnv4oFZfk0PrOO9i3bmPUlWnEpBqQBDx4lW5iTX3xM32cX/qZDXi9UTSYRJTu1h+NoWmqtCETvThD3URJJThV2QgyBSOSf54FQ0qEjv5mAw73EE4kgNFaSlOt+3vzj+tKrIgihFiL2ZnkZ0JAyevVsVyTH99lefD9zXqiDEoOynLJczo4EGFHW32UYFDEUnP+c8dPI4oie74swxCuInN4FM4DB2j/8ktC589jt1CG0+/kYrsLUWng1UozE/tF9m0MdzI6pYyr8uN4+aSeGUIISzNciFIJEZbD+DwBqgpbLsg6gkGRAytK0LeXszutmGPWWfxiUu8cfekrUvvoo5eikEn4RUEa+2scnEy5mTmNNRyVN6DMVKJ1N3Jsc3Wnfl51UStSqYih5RQro+oY5XSxUxhNUpj2gsUG9NHzCNEoeG/BCPqZ9dzx7j7WFDbDzJe4OnYCV7Xb+H32cbyJ0dQ9+CBiu5Up18SQv/8pvCoZsX0ZqX2cZzKi9LR4EmgMEVG5LVjrfljm2VjZjt5WxYkoL0McNjZ4+jE8ORS14udbMEzLieYTy0BCtD4U3nICQeF7Zzpri1sRxCASRSM2o4haNQ4ECVfmd90mpyAITMyM5HNrOnkOG4ci3ejtVcCFNU+qOGKhqdJG3qVJCAI0PPE3ZBERhN96K6vLVxOiDGFY6Q4ssRNpcIpMyYq6YGv7OXHzqCQCoohWOQ2fWqS2nwnVxiXIVVLKLpDkt+xgE+1tfmJq1mLppyEmbRI5cb2viwp9RWofffRqZg2JIzFMw2/LBzFeVBOPnM9zXMRUb6ah3EZzJ9ro1xS1EiZpRdAo2RHeRoHDwWutuVwxOK4vp62PH8SokfPOguEMiDFy13v7WF1ogSsX8aBxEDkBL49ObsXX2kr9w48QaGtC56jDpdQQ01ek9nGeMarltKlTQR9E7bZg+wF33/9r787j46zKho//rpns+741bdI0adJ0X9KW0g1oKS2LUlEqO4jwvrghKoKKis+j8urjhj4+iIC4APqICwIitOxLoQvdS9OmSZs0+77OPuf9457YUNJSaJZJ5vp+Pv10MnPPPWcyJ2fu6yzX8fn8tNZ0E9d5hL2Zbua6XDzZNSVk16P2Wzczm/0mjwWeMGoSKwCoH2Rdam15Owk91eyc1MtSh5P7mhdwTnEG2Ymj+3e+sjiDF1wlLHC6OJwNke52wm3eEQtS/70WNS2K4sVZdD35JM7du0m/7TYOuY6x8chG1qbOIczRxossJCLMxspizQExHPJSYzmvJIMfHJ3DBQ43TxR24K87Rm62tTWQ32+G9fWNMbz9dAVRjmb25O7iUM8lfO688TmKChqkKjWuhdtt3LqqiJ0NbionX8nVLY08kXqETA5jM172DdF2NH1dbtrqekms3UFTaSbYhXm+JHb5C7h0rmb1Ve8vMTqc339qITMmJPKZR97mmXfaCN/wKD8Oz6M7xcUTKyPofu45Gu/7OQCOqETdkkuNjIwSYmM9RDlbcbgE70nW83c09OHzQXx3DRU5wkyPnb1mMitDdD1qv4L0OEqyEohxF7Ivt5soRwt1e+rfdYzH5aPpaDdJbeVszvey1B/Hqz05XF42+snRzi5MpdueSGREPgnhYbTlxpPoaR6xIPXo3laajgZGUV1Omn70Y6JmzCD24nV8641vkRCZwC2eCIw9kl/VTmZZYZq2jcPouiWTqe4L46zI2bxRBL7oCNKad+Lo9pwyc/VQqK/opKnWSe6xF2iYEUbi5LXMmTh+Z6ppkKrUOHfJ7AkUZsRxR/UiLnEZ4iWMtxdGk9G4nfLNdbidZ76/V215OwCJNdt4dWI38x0u3vAtpiw/JeS2XVAfXkJUOL+7YSGzJybx2cd28NSBDlKveJyf+VL421wnRwui8bzwOgCeeM1cqUZGUm4xWTY/xm9tM9HVOniG3/6gJdpdS18SOG3TyEqKY0p63IiVNVhdODObt1oWUDnRkNhZScORHow5PurUEFiPmtB9mIMTwe1dSnp8VFBkhY+PCqcsP4XXvTNY0NvL/iwPsY0HaK3rGXRt7VDqX4san2qNorY+8ADepiYyv3Ynj5Y/xr7WfdxZdgdJB5+je8JSKjphzXRtG4fT2YWpFGXE8Vzn+eQaL3tLwoh95c/Y7ELlruGd8rv9qQrCPD1UpLzJUedaPj+OR1FBg1Slxj27Tbh1VRFvt9ion3gZH+/o4IGJleQ0v4nHbYYkgdKx8nbC7T7ieo6xMaedVb29/L57HpfO1YRJ6oOJjwrntzcsZN6kJL7wx538o7yX0que5G5vNPdc4MYRGZg6nlIwugVVIaM4O5kYdyw9EdY2NF0tg2f4ba7uxm48NKa0M8fl4LneYpZPTdflDsC6Wdm85Z/JxGgvka5KnG7bu36PtYH1qC2JR5jjc/GrxnlcNj+X8CDZgu+c4gye7JnKfEcfuzKdxLVX4vca2uqGN3lS9b42mo52s2BtPv7GBloffIiEdetoLUrnFzt+wYrcFayJyoGOajaHn4VNYFWprkcdTiLCdWfn87emHC72RvP3Eie2rlay07xU7Wx+V+fLUGqr66W6vIsJtS9TM9tP+MSPMj8veVheK1gEx1+/UmpYrZuRTUlWPHfWL2dDTx99MdA5zUdsXwN7X6o54/MfK28nxVWLY1IK7fHCPG8ih+wFXDgzewhKr0JNXGQYD1+/kPl5ydz6xx38vdzB2iue5qORNn52sfD8bCEmbdpoF1OFiOKseIw7nbYYK3tn90lGUpuOdBLXVcO+TCfznC5ecpeE/HrUflPS45iclUquJ4O6uEoA6iqOT42s3dtEXHc1W/IdLPAnU2WyuHzB6E/17XdOSTpb/CXMdXk5mCPHkyfVDN+U3/61qPEp1ihq03/9CID0L93GdzZ/B7vNzjcWfwMp/yeIjQeailk4OYWU2IhhK5OyrJ+bS2J0BHb/eRycCI6kKNIad9DV4qS1duhyfQy045nD2PxuGqNfpc57Lp9fNXVYXieYaJCqVAiw2YTbVk9la1s0jqy1rO3t4+Gp9eTUvkLzsd4zWlvT1eqgq9lBQvU2dhTATJeb150LWTUtk8SY8CF8FyqUxEaG8fD1ZSyanMpt/7uTvxz08LlPPEFsnvCHC4S0dA1S1ciYkh5Ht2sSjUnd2HxuOgfZhsbvN7TUdBPfXU1FjlDqCeOg5OlelQNcODObmvZZvJPTQJi3j7pdVoZ5r9tH07E+kjsq2DUZmrsWcVZBKvlpsaNc4uOmpMeRkpyMkSm4kgSxdxEmXpqPDl+QWr2/jcaqLuavzcO1exdd//wnqZ+6gWcc23iz/k1unXcrWRGJsOd/cWSVsbXZzgU61XdEREfY2bBwIj+vXcg5Tjcvl3iJf+tvIFC5Y+in/PZ2uDi4rYns+s0cnt2DN/sTlOWnDPnrBBsNUpUKEatLM5mVm8g3ms7lqo5O9mW4SIiqwub3sPeVD59AqbbcytKY3PoOG3PaOa+3l8edZazXqb7qDMVEhPHQdWUsmZLGlx/fxV8Oh/GF5X+ksOpiJqSN72lOKnhEhNnwxJXQnegnytVG57H29xzT2dSH1wvxPTXUZAlO71Tm5qWSEKUddf3WzcrmLefZtEzwkdBZRf0h6/fYUNWF3wjh7grS4jz8pXMJGxYGzygqHN+KZqNjGvMcfVROEOId9cM2ktq/FjUuJZLiRZk0fu97hGVkwFWX8oOtP2Buxlw+UfwJePbr0FbJpvRrADhfg9QRc/XiPDpMLPOZysbphghHB+nxbip3tgz5a+3aeAS/H3p4kSbbMj6/umTIXyMYaZCqVIgQEb64eiqvd6YRl7yMhW4fT83oJLNxGwff+vAJlGrL24kUN1GmjYMTYI47kYboIlaEeEZLNTSiI+w8cO0Clham8dW/7Obn29285l6m28+oEWXLmEZ4vJcoRytdze9dk9o/G0X8tUwMd/FCX7FO9T3BlPQ40rMmkhETSaSzks7eMJy9HmoPtILxcyj9MDO8qXii04Iy+c85Jem87CllgdPF7kw3cc0Haanpxu8b+uRJNe8ERlEvyKfnn0/h3LuXjC/dxg/23IvD6+DbZ30b24GnYduDsORzPFA/mVm5idoujqDc5BjWTM/iuaZVeFN8NGVFkNqwjdbaHjoHaSM+LLfTy96Xakhv3sk7s5vpzbiKxQWhMUNDg1SlQsjKqenMm5TE3W3nc217G88U9ZDVvgWvBw5uafzA5zPGcKy8naTOQ1QURpHv8/JWbxmXzJkQNAkv1NgXFW7n19csYHlROk/ttraumKAXY2oEpU4sJjbGZ+2V2v3eLWiaq7ux+T0cSa5jrtPFG/7pGqQOYt3MbNzdU2iOqQKgobKTYztries5xs7JTqpby7h07gSiwu2jXNL3OqsgjXfsU5nlhkMTrK2GfF5De0PfkL7Ov0dRkyMpnp1A849+TNSsWeyYE8+/jvyLm2bdRAHh8MRnIWcu9Qu+wq6ajqAM7Me765bk85KziHXOcJ4t9ZK8+18AVA1hlt99L9fg8dkwzk20Ri7gs6tCZ6mLXkUqFUJEhC+fX8zz3RMpiJ5JjsCB4jbieo6x78WjHzgrXWeTg94OF0n1u3gpt5tVvX38w7NI90ZVQy4q3M6vrp7PuSUZJMWEkx4XOdpFUiGkKDuFKFsMNm8rHp8dV5/nXY83VbYT11PL4WxDiSeMtpgplGYnjFJpg9e6mdmU9yzjUOYRxPio3nqUpgYPSZ0V9OX4eNmxLCj2Rh1MdISdBQWZ9HqKaMgS4nqqAWga4nWpx95pp6Gyi/lr82n/zYN4m5tJ+MoX+M8t36MwqZBPTbsW/noT+L3wsQd57oA1bVqD1JG3cHIKpdmJ+HqX8Po0iHK1kRTpoHLn0ASpPp+fHc8cJqnjIPtmHqUj9XqWTAmNUVTQIFWpkLOkMI3FBSl8v2MtV3e088dp7eTUvUZLvfMDf9keC+yPmtxxkB0FMMsZjye1hFm5icNRdBXiosLtPHDNAl65/RxsNt3WQ42c4qx4cKfiCHvvXqnGb2iu6SG+u4bD2eDoK2R5cbrW0UEUZsRhS52LI9tNXM8xDrzdjt/Y6LUfYpJJZcrEHKYFcXB/TnE6zzunMc3vpCu+HbvxDOm61P6MvnHJkRTm+Wl76DckXHQR9/lepKmvibuX3E346z+F6jfgop/gTy7gH7vqKMyIozBD9+Mdaf3b0TzStoJp4W4O5NtIrd9G/eFO+rrcZ3z+Q1sacDhtRHVtoi1hBresmhlSW1ppkKpUCPrS+cU83VfMWUzAkSz4Eiqw+93se/nYBzrPsQPtRPl76EnpIzrKx9s9C1g/f2JINaJqZNlsoslo1IjLTY7G7Z1ER7QVpHa3HA9Su1odeDwQ11ONPdXLFnepTvU9hXWzcvBGZBDVV4nHa12GlmdVUtO2kA1BOorab2VxBm/4ZzDf6WR/toe4nmM0H+0asvMfO9BO/eFO5q3Jo+WnPwabjaZr1/Cn8j9x5bQrmdXbBa/8AGZfAbM+wU82HWT70XauXZI/ZGVQH8wls3OQ2DRmuyfx/HRDyuFXwZz5lF9jDNv/9g6xvfXsKd5PS8KnWVaUNkSlHhs0SFUqBJXlp7CsKJ2f917I5Z2dPF7aREbDVg5tqcflOL0ESsZvqC1vI6l5H5vzHKzq6+OfvrP4qE71VUqNMyJCWGwprXFWkDpwG5rmaut2X1gN0/0uNptSlhVpkHoyF87Korl3Hu2R1n6psT21HJvUw17fOVw8O2eUS3dq+WmxuFNKmOYK51COEN95xEqe5P9gS2UG0z+KGpsUSX5MA93P/IukG67jW4fvJTs2m8+VXGlN802eDOt+yBM7a/n5CxVcvmAiVy2aNATvTn0YUeF2rlg0iVcbzuVgoZ9Idx0xdgdVu84sy2/1/lY6uoTE1k10phdy8+o5ITcAoEGqUiHqS+cX83jfPNa6E9hZCGndW/H6hINvNZzW81vrenH2ekluO8D2AqG0L46U/Fma0EYpNS5FZE3HG9+N3eugo7r13/c3VXchfi81yQ0UucOJyyklJTZiFEsa3Aoz4nHFXUB1uhWkxvQewhabytpZ+cRFho1y6d7fipIsmvumcjTHSp7k9Rg6Gs88eVJteTv1FZ3MWzOJlv93D2GZmfxtoaGqs4q7Fn+DmKdvh54muOwh3m708JXHd7Nocgr/8dEZIRe8BJudtjVvAAAfKElEQVSrFuexldms8NrYWgRpdduoeacN92l2+g9m25/3EOHqYG/BVhrjbmJlCO6YoEGqUiFqzsQkzp2WxR/61rK2r5dXi44Q313N3uePnFYCpdrAetRYZyWtmT7Ke8pYPz+4p2oppdSHlTppGmEJfqKdrXQ2HJ/i2XSwhdjeOipyfDh6JrOiOGMUSzk2nD9rMl2ZLkr3P0xr+CYauhdzeZDtjXoy55Sks9k3g/RYNxFua4lM/xZEH5Yxhi1PVRGbGMGElm049+3D/3+v5P5Dv+WigotYWncAyp+G1XdTG1PMTb/bTnZiFPddNZ+IML2UH22ZCVGsnTkBb8c8XpohpNVtxe8zHN3X+v5PHkRzdTcNDYbU5hfpzJnADavKQrIjQmu2UiHsi6un8ojzbD7Wa+fZWX6yG16nrdlNY9X7r7GpOdBGjKuVAzntrHA6eJ4lrJ2h2QWVUuPT1JwUiAknytFCd7uVFMUYQ8uxXuJ7aujK8LHXMyskRzw+qAtnZdEUNpXXC7fx6rwOXLFrmTsxabSLdVoWTk5hu20WC1xOapIasPk9Zxyk1h7soL6ik7nnZNN674+Jmj2b78S9SHx4PLfnXQzPfh2Kzqd37k3c+NttuDw+Hrx2Ack6Yh80rjs7n6d71mDL9mDzVhGB80Nn+d36p13YvQ7Kc1+jIfrTnDctNDu+NEhVKoRNz0nkvBkTeb5vDdPsThqTdmH3udj7YvUpn+f3+akrbyOpdT/bCgylfXFMmV5GvCa0UUqNUyVZ8XhtyYR5Wulx2DDG0NPuwuURYnpryI5zsTd8FrNzx0awNZoKM+JxRq7jTyvs+GLSuXxhwZgZKYoMs5NXWEq+M4ZDE/zE9RyjqarjjM65NTCKmrHvKXzNLey8Yh67W/fw1Xm3kvyPWyE6Cf8l/82t/7uL8oYufnHlPAoz4ofoHamhMG9SMjkTJzPDkcGrpYbUhrc5uqcFn8f/gc7T1eKgqsJJetPrdE5K5KrVS8fM38ZQ0yBVqRD3xdVTedi9kg09Xp6a3Utm4xYqtjfh7PWc9DlN1d143Ibk9oNU5BmOdJWxfp4mTFJKjV9JMRHYzCTc9lb8hOHoPj6C1hFZQ4EnnMlF07Hr1jOn5ZyZi0moXcP+1htZP8YS7q0szqC5t5jKHCG+u5qWmm7Mh0yeVFveTt2hDmYtSqTztw8RvnYV3+95nKUTlrLunReh5SCsv58fvNbGxv2NfPMizR4drG44O589reextRTSm3bicfn/vVXf6dr++B7EGKrSXqQu8ibOL80cptIGPw1SlQpxUzPjOXd2IQe6V+DM8hHjeBOfXyg/RQKl/vWovREVzLY7eStqBUsLQys1ulIq9ETHzaA7og2wRjyaj3SA8VGdWoe7V9ejfhAXzsymtuscVpbMGHPTVlcWp7PFN5OIVDfx3TV4PNDZ7PhQ59r6dBUxiRGkvvQbsNu57ywrW/RdKQuRHb+DpV/k8fZC7nv5MFcumqTbzQSxtTOyORS9hClxBoetHLtxf6Apv84eDwd2tpPWspW2KcKG80N7T3ANUpVSfOG8In7jWcOV3X1sKqkmoesIezdWnjSBUs2+ZmJ7atmZ1820nhjmzF1ImF2bE6XU+BafNYe+GGtria7GHhreaSS2t4G6LDcHnbN1hOsDKMqM5571M7l9TfFoF+UDy02OoTF1ITONC4e9BvhwyZPqDrVTe7CDGSVC33PP0PqxZTzr2MYXSq4h59lvQW4Z2wr+D3f+dTdLpqTy7Uumh+zUz7EgIszGFWcVIB0zeW26n9Tm3VTtaDztLYp2PrkfP+HUxT1PQ+QNXDA9tPN86FWlUoqC9DiWz5tOX1cZu0v9ZDa9Tke7j/rDne851ufx03C4i+SOcvZNhrruMi7Vqb5KqRCQljcNX6wVpLYfaaal3kF8Tw2xKW5a0heRkRA1yiUcWzYsnMTElJjRLsaHMnfaVDKciRxNaUD8XpqOvn/CwRNteeoIMQkRJD35U2xZmXxz0nZmpc1kw46/AYa6837BTY/sJjc5hl9eOY9w7QwOep9cOIlXnRdRW+QlvXkXjl4fDZXvvZY6kdftY/crtSS37aOtqIuPrl4X0qOooEGqUirAGk29mI+4+ihP247d62Dv80fec1xDVSc+vxDTe5CMZBcVyedTmp0w8gVWSqkRNjUnFX+cl3B3Nw0V7TjddsJcNaSFh1NaMn20i6dG0MriDFp7S6jM8RHXU0vToZYP9Py6Qx3UlrczLasD7/69vHRRLm3Sy932bOzHtuG44Edc//cmvD4/D167gKSYsTUlOlSlx0dSNns2kySJppj9iPFSueP9p/zu23gIj4miI3wj9VFXc+HM7BEobXDTIFUpBcDElBiWlM0nuaOEF+d6yGrcwuGdrTh73p1A6diBdjB+jiYfprgvhoULFur0I6VUSCjMiMMZm0i0s5W6OitrZ3t0Df6+PJ3qG2IW5CezxzYPf7qX+J4aWmr7TmuP8X5bn64iOj6cxL/9CE9pAb9I28mnc1dT+OaD+OdezWd2TaaiuYf/uWo+Belxw/hO1FC7/ux8jrSfx1vTXKS0lVO5tfaUdcPvN7z9z0PEdR+lqaiGC1d9TBOwoUGqUmqAz55TyKOeS5kd04vX+zp+I7yzue5dx9TsrCW+u5o9+Q5aexbw0TGWlVEppT6sqHA7/uhcwl0t+I2A8XMspZZ6z3zm5yWPdvHUCAq324icsowJsW6i+qpxe4WuFudpPbeuooNjB9qZGnkEmhu4d3kPBQn53Lj9CUgr4odczwsHmrj7kumcrUkJx5wZExJxTLgA+yQ/yW076e7y01rbe9LjKzcfpc8Xi8u3ieaYDVw0S0dRQYNUpdQAOUnRzF+0jKkdubwwo56Ezsp3JVDyuHw01blJ7jgIE9y051xMpq7BUkqFkIT4GfikFYAYRxP+9F7IX0FEmF5ShZol0/JIcqXQHn0MOP3kSVufqiI61k7ykz+l+uwpbElu5+4+iHC086+S7/E/mxu4bkk+Vy3OG87iq2F0zdIiwhwlHEvcC8ZP5fbBd0wwxvDWn3cR5WihqWgf55x7hSaiDNDfglLqXW5ZOYWn3OuxT3KR2vo6XV2GuoPWRuX1FR0YBBfl5JkYli5aOMqlVUqpkZWWtQBHmBWkxnfXEJ5oZ05pySiXSo2GFcXptPZN50haHeL30VTZ9r7PqT/cybED7RQ4dyHi43tzj7IhaQZzKl6lat6dfPYFD8uK0vjGhdNG4B2o4XJ+aSYH5TLemdpJYmclFZurBz2ubl8jHc54xPkCTXEf0dlpA2iQqpR6l4yEKKYvvoCyngR25bxNmKePPRsPA1CzpxHxezmQVUlv7wLWhHh6dKVU6MmcPBNPpJUkx++rweufpOtRQ1RmQhStiUvoy3AR21tH44Gm933O1qeriIoS0l58kE3L4wlPS+ELezbRN/l8Lt1WSl5qDL+4Yp6Opo1xYXYb5y05i6jUWOI7d9PeYe2tfKLNv3+LcE8PDVO2sPScT+nnPoD+JpRS73Hziilsdqzn8HQnmY1vUbm3A0e3m5qddSR2VdE90YFMuYyYiLDRLqpSSo2o4gmp+KJrSG4vpz1qDy774jG7jYo6c1mly0hM9hDfU01Lo+uUCXIaKjup2d9GXtOreFIieXhWO99s6yQ6OoUrm68GER68tozE6PARfAdquGwom0hL37lUpewCoGJL7bseb63poLEznojuV2hNOI9L5+eORjGDlgapSqn3SI2LpGDJespMGF321zHY2LnxKK0dQmz3QRLjolipU32VUiFoYnIMznhh7q576UltILFo1WgXSY2iFaU5hJOB8dbg9trpaXed9NitT1URGe4na+dfuH+Zk1UxGSxrOsL3o77EnrYw7rtqPvlpsSNYejWckmMjSJt+Oe0FTcT1HKPilcPvenzzA69g87lpnPQyZefcovvgnkB/G0qpQd20vIjKvkvYOr2exI4KdmysBoSm2IMY70LOmpI62kVUSqkRZ7MJ3dkTeXqB0DkpjLLpxaNdJDWK5kxMptczm+Y4K3lS09GuQY9rqOyken8bk6o3UZcXwZ7SSL5asYNXsq7j1zU5fPfSGSwu0O/V8ebqpcW4oguJ6tlFc3sYfV1uAHra+qiujyK26y1a08pYP1+TZJ1Ig1Sl1KASY8LJXXIN+al+4jtewxixevyyD5M87ZO6h5dSKmSlJc7kd6tsIHkaWIQ4u00Iyz2PzpRqxPho3F8/6HFbn64iwuYhu+Jf/PcKB7e3tOJLmM0NR87lxqWTubxs0giXXI2E4qx43ElXUZO6C0Q4/Ko1mvrWgy9hxEZT9vPMXn6bZgcfhP5GlFIndd3yQnr6VrMjbyfhnh6SOiuwZ4Rx3mKd6quUCl1Zk1bzRE0jMQkXExVuH+3iqFE2dfZiwtLcxPQ20Fj+3uRJjVVdVO9rY2Llv3hzup+szAjWOjx8rOl6lhdncec6zeQ7nq1deS59E5uJcrRw4KVyXA4Phw4a4jp30ZpVyGULJ492EYOSBqlKqZOKjwone8nNhBU4mLn7Z0S3PYZELaQ4K360i6aUUqNmQsE01vXeR/qsC0a7KCoILJ+aiSsmk9jeGlpavO9JnrT16SoicJHR8DJ/XiHcVXuE292fJjo9n3s/OVdnJo1z55Zk0BN9HnbnLpo7Y3jrgZfx2aNpTd3I9LO/QmSYdnQNRoNUpdQpXbVsGnbfYn6/sp7nl7QyeeZ1o10kpZQaVQvzU7h17Rw+vkCzcSor2WB45EKcthrc/kj6Ot3/fqzxSBdH97aSW/kMf1/k4ipXK9tZzcv2s3jw2jLiozST73hntwnTFt1IR/JOjC2MPXshtvsQHROSuGzx1NEuXtDSIFUpdUoxEWHkLr6N0hwHJXFRnHdW2WgXSSmlRlWY3cbNK6aQoAGGCkibehHdsUcBaDzc9u/7tz5VSbjfQUz3a1TNMSzvS+brjk/yq6vn69ZFIWT9oiI6ctyEu7tBbHTEb6Rw8R26XOAUNEhVSr2vjy+bxb6+L9KQ/A3S4yNHuzhKKaVUUJk/Zy6+5Dowfup3HAGsTL9H97Yx8chGHlvh4o72dm7q/QzfXr+AsvyU0S2wGlGJ0eEkFd2MuDYT21NJx0QvH186c7SLFdTCRrsASqngFxVu5zufv5mocO3XUkoppU40IyeRp1OSyapo5NjBaAC2PFFBmLePHl5memY3j7Zs4NzlK7lsvk4TD0UXr7qAx47+B+22PmbPfZToCB1FPRW94lRKnZb0+EhdO6OUUkoNwmYTEnJWEOGqobPTbo2i7u9gYs0mnl/uIK99Kk1Tr+D2Nbqvbqiakh5HX8o36XHewmUr5o52cYKejqQqpZRSSil1hgpnXUrti/fjkYW88rs9hHl6qUp8hfV2L79O/hIPb5iLTTP5hrSvXn0JfW4vMREagr0fHUlVSimllFLqDC2eWYo7pgaAxloX2fUv0DuvnT95P89Pr19JbKQGJqEuMTqc7MTo0S7GmKBBqlJKKaWUUmcoMSYcd7oXgDBPH+9MeAkcK7jlmquZkKSBiVIfhHbpKKWUUkopNQQSZ64g5s9vYndXwlk2Mtd9h/l5yaNdLKXGHA1SlVJKKaWUGgKLllzFI1tXER3nI2XWA3x03qTRLpJSY5IGqUoppZRSSg2B4rwckrMvQdJKuOmiZaNdHKXGLA1SlVJKKaWUGgIiwq1fvkez+Cp1hkYsSBWRcOCHQBWQZoy5a6ReWymllFJKqZGgAapSZ24ks/t+Ckg2xvwMWCEiq0bwtZVSSimllFJKjQEjGaSuBhoCtxuANSceICI3icg2EdnW3Nw8gkVTSimllFJKKRUMRjJIHTj3wZzws3WnMfcbYxYYYxakp6ePXMmUUkoppZRSSgWFkQxSNwG5gdtZwPMj+NpKKaWUUkoppcaAkQxSfw10iMhngdeMMc+M4GsrpZRSSimllBoDRiy7rzHGA3xmpF5PKaWUUkoppdTYM5IjqUoppZRSSiml1ClpkKqUUkoppZRSKmhokKqUUkoppZRSKmhokKqUUkoppZRSKmhokKqUUkoppZRSKmhokKqUUkoppZRSKmhokKqUUkoppZRSKmhokKqUUkoppZRSKmhokKqUUkoppZRSKmhokKqUUkoppZRSKmhokKqUUkoppZRSKmiIMWa0yzAoEWkGjo52Od5HGtAy2oVQQUnrhjoZrRvqZLRuqFPR+qFORuuGOpmxUDfyjDHpJ94ZtEHqWCAi24wxC0a7HCr4aN1QJ6N1Q52M1g11Klo/1Mlo3VAnM5brhk73VUoppZRSSikVNDRIVUoppZRSSikVNDRIPTP3j3YBVNDSuqFORuuGOhmtG+pUtH6ok9G6oU5mzNYNXZOqlFJKKaWUUipo6EiqUkoppZRSSqmgoUHqACISKSJTROSUvxcRKRSRmJEqlxq7TlWX3q+eKaXUibTdUEopFQr0y+7dMoEK4IITHxCR20TkLhER4A/AA4H7I0TkyhMvHESkQEQqReTGwM8TROSrw/8W1FASkXQRuVVEJopI7gn/viEiPxxw7F9F5NuBOnG5iKQCN4vIXhGJHnDcDBEpAL4lIkcDr3GRiEwYjfeozpyIlIjIx0QkcgjOpe3yOCAi60Wk6sS/60Bb8n0R+aKIvHSSf68E2oY1A56n7cY4ISLLRCQ8cPseESkSkYTAzxknec77tgvadoQOEfmIiNwcuB0Z+H+qiHxzsDqkdWP8EpGFIvI1EckLxChDcc6gqC9BUYgg4g/83zbIY08DscA6wAs8KiKrgA1AOlAoIl/pv8gAHgImA78O/LwJuFtEPjm8b0ENsUjgJ8B5wD+Be4G3geuAVODTIpIZODYRECAC+CNWvTBAnDHGAf/+w38UeASIATyB47OAAyJyx4i8KzXUyoBfA+7+OzQICW3GmL9ifaesEZHNIrJbRFqAPwFrgbeNMSuNMSsDT3kTyAceNcYsN8bkGWOeBW03xqF1wO9FJA+4Eeu74xYRqQDeEZH/6D9QRN4QkQ3AkyLy3cFOpm3H+CEic8UaFFkc+Ox/KSLni8gjIpI24NBlwKrA7W4RORvIAO4GmgecT+vGGCQiXxeRtwLXC6+KyMuB29UicvEJh88BvgN0AK9IYHBswLlO2WF6wn1BVV80SD0FEblPRIpF5HysQOUx4FygFPgy8A3gF4DHGHMQ+HH/RUfgwuMwcGfg9gJjTJQx5rHReC/qjP0dq+F/A+gDXgK2Ay3GmMbAMYYBQQrQM8h5vgs8CJwdOE80VgfGZ7DqWO4wlF0Ng0DbsDTwowH8xhgjIokiciHwgAYhoUlE4kXkI8Bc4ACwFPgx0GOMWWKMmWOMeVlE1oi1dEQ43jnqFpHPiEjcgFNquzG+PAHUYnV8A1wBFACbgRex/vb7pQBRWG2MR0SiAsEtoG3HOJQI3Ao0AA6gG+va4wog+YRjewP/24DW/jtNICOq1o0xLR4rhjgKhGO1GTaswY/2E45NAY4YYzqBh4HGQCd5OLx/h6mILIfgrC9hI/2CY8QvRaQrcPtW4HNYH1oZsBFYgzXddzqQBGSKSKwxpldEfg04gZexvoCmicgVwEMicpsx5pcj/F7U0IkFak7xeBjWl8qgRCQF+BnWBUcZVoPzJvAboAt4yxjjGrLSquG2FrgMKwAZKAV4ClgkIsnAqwwShAC/Ncb0d2T0ByE/B77F8SDEiQYhY44xpltEPgFMATqMMW+cZBbWXVgXAOlYy02ygDsC/68BLtF2Y3wRkUSsjosDwC+xgpIVWAHIS1h/+85TnOJ64D9FZIUxZi/adow3NqwO7qVY3yV5wCKgM/AzIrIaK3CZFxhEAeu76O0TzqV1Y2xrAi7F6qgoA74CPA8Q+Ny/FjhuMpAUmLUJ1nfFIqBIrGWG52J1mM7AqldXA982xiw54fWCrr5okDq4W4wxbw68Q0SmAH8FJmBdUNwCxGH1fn41cN//xer9CDfGPC4i9wB7jDGPisgjHJ9OrMYeO5CD1cvtA6YBaSccE4n1RTIoY0ybWGtTvwR8AdiGNfL6ZWA2sAdYPuQlV8PJO8h9/ft69WGNnmkQEpquBm7Hmn1xKv8J/AdwJfAX4B6s5QRbQNuNcagP2A3sAlYD9Vh1YBqwAKvju0BEXsNqXyYNeG4i1md9HbBf247xRUTKsDrDbwvc9QpW/TgK3Al8QUQeBr6JNZrWjvX5A8wDzh9wLq0bY5sNq+OhHaujIh7Ixvo8fcALxpjnAETkd1iB7MeAywPPmWGMeTTw+Pt2mAZrfdEg9TSISC7WfO//xmoIooFjwFbg98BnjDHPjF4J1Qi4Fvi8MaZJRO4FLgQSTjgmBag+2QlEJAdr7UA71hfP7QMe/i/g4JCWWAULDUJCTGDt2HeBs4wx97zP4YuAZ40x20Wkg+OdGJsC59J2YxwxxnhEZDHwI6xpnL8C/hf4B9bnGwYcNsZcDSAiBwY8PQ24wBjTv6xE245xxBizVUSuwWo7Dgfuno01mjYb67qjGytYqcFK9PkU8CzWIEo81lpV/V4Z+45hrS8+hDXj4mGsa4brgDJjzOYBx07FCjA/hjVr6z6skdU3jTGVnEaHabDWFw1ST08dsBCrkkzFGm7fjzWSGgcsEZHXjTFdgzw3KrA2CY6PsKix5wmgVkSisC4qfgV8Evg6QGBdWSLwOsfXer9rzbcxpg6oC/RYpWMFL/2mAnuH8w2oUaNBSIgxxrSIiA/YcRqHdxhj7gzcXoUViDw04Fzabowjgem+j2EtCYoDfoh1UShYa8CSTvH0wwMCVG07xic/YDPGrAwsH9uLdf1RBbiNMVuAKSLyF+CgMeb5wMjYNgbM7tK6MXaJ9YH+juMztXqwPq9tWPXhP0UkyhjjDOQuKMTqrPgJVnsSBqwwxjSfbodpsNaXkA9SRUT6F5kP8lgW8FvgExyf4hkFXINVecKwLhpWYzUgDwX+rx9wGifWAuR6YOfwvAs1jMI4/tlPPuGxAo4nK/gkcKsxpi8QsP6a42sQ7YOct3pAQh1E5F9DWWgVVDQICU3xWOvG3iNwgdm/nuhSETnrhENKsbKJn0jbjTHOGNMpIruA/qlzF2N1aBZjjZzkAojILKz24j1Bq4gUG2PKte0Yl4TjAxpnY3V8D3ysXwnWusFBad0Y0+4CLsJaGkDg/1uBLwKzsJYLbMAaOLsOa5anA+t69QBwM5AnIn2n22EarPUl5INU4A4ZsA0EVu/mPSfM2d6A1ZPRYYz5CFj7ImIFsM8CFxpjfAADLkbBClBbAxl9Navv2HQUSDnJKPkm4K5AZ0a1MWYjgDGmD7gJQEQ8WGnBB7IDGQMWuYMVBO8e4rKr4VUIzAl8jplAYuB21AnHaRASmhxAh4jchDVtKgz4gYhch7W2PS9w/23GmHfVBRF5Giu750DabowfU7G2MDuKdW1QB3zUGLMrMFvHYCXB+STHvz98wCQRSQe+LiI/CCRO6qdtx/hwAGu7wnOxRtrrsdagbieQ80JEFgLbB3z+Xt69s8CJtG6MLf8Pay36V7EyuP8RK4HrX7ASrH0N+HMgV85srKUD92EtRXwVa13z3Vijob2cRoepMaZ8wN1BU1/kJIOISimlTiGQuXe6Mea1E+7PxJqqcz3wB+C/ThKE/M0Y88CA+67Fmq4zMOiYhpUFeODUG6XUGCYiNwMVgamapVh/509irSn7EVYOBGOM2RRYk/ptrAvQe7EuQBuBm4wxTwbOp23HOBCYmjkfWImV4+L7xpgjge+ax7Bm6t2Dte70j8YYb+B50cYYR6Az9A9AkTHGH3hM68YYE+jI/CJWXfgJ1npQP9bSof+D1ZFxDdayoVqspWbPA/+D1XZEYHVofNcY818icj/WzI09HO8wjcAaoY3Dmjben4QpqOqLBqlKKaWUUqPgVEuOlFIqlGmQqpRSSimllFIqaNje/xCllFJKKaWUUmpkaJCqlFJKKaWUUipoaHZfpZRSapiJyF1AmzHmvwM/r8fatmr5gGMexdrqynXC06cALwCfMcb0jFCRlVJKqVGjI6lKKaXU8MsDskVkgoi8ipVd0S8iYSLS/12cAWwEzjXGrAxsA/AiVsbOmzVAVUopFSo0SFVKKaWGkRzfeLsQuAJYirWPdhzWtiIVIpIYOMYJ3CAil4tIBHAjkGiMcY5wsZVSSqlRo0GqUkopNbweBC4EzgLWB+5bihWkvgFEG2M6A/f7A/vnpgHvAD7gjpEtrlJKKTW6NEhVSimlhpEx5gbgaeAR4PeBu/cADUAX1ujpif4OJGEFslNGoJhKKaVU0NAgVSmllBoZfuAfgdudWAHox4G6gQeJiB24H7ga2AxsEpHMESynUkopNao0u69SSik1jEQkEkjAGhGtCNxtBxzAM7y7wzgM+BpwhzFmj4hUAfuA2cBzI1ZopZRSahRpkKqUUkoNr/uw1qNeDhwO3FcBXI81onq/iLwJRAHZwG3GGBeAMeYdEbkRuFVEXu6/XymllBrPxBgz2mVQSimlxi0RmQz0GGOaAz//E1gOhGMlRmrDGj1dC0wP/DyYB4wxfxj+EiullFKjS4NUpZRSSimllFJBQxMnKaWUUkoppZQKGhqkKqWUUkoppZQKGhqkKqWUUkoppZQKGhqkKqWUUkoppZQKGhqkKqWUUkoppZQKGv8fhIyApLNINQcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['font.sans-serif'] = ['Songti SC']\n",
    "分进合击进阶.plot(figsize=(16,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 结论"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 广东省的地区生产总值近几年来一直稳居第一\n",
    "- 西藏地区的地区生产总值较低，经济环境相对落后\n",
    "- 因此，追求经济发展的毕业生可以考虑向广东省发展定居\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 各地失业人数分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>地区</th>\n",
       "      <th>上海市</th>\n",
       "      <th>云南省</th>\n",
       "      <th>内蒙古自治区</th>\n",
       "      <th>北京市</th>\n",
       "      <th>吉林省</th>\n",
       "      <th>四川省</th>\n",
       "      <th>天津市</th>\n",
       "      <th>宁夏回族自治区</th>\n",
       "      <th>安徽省</th>\n",
       "      <th>山东省</th>\n",
       "      <th>...</th>\n",
       "      <th>湖南省</th>\n",
       "      <th>甘肃省</th>\n",
       "      <th>福建省</th>\n",
       "      <th>西藏自治区</th>\n",
       "      <th>贵州省</th>\n",
       "      <th>辽宁省</th>\n",
       "      <th>重庆市</th>\n",
       "      <th>陕西省</th>\n",
       "      <th>青海省</th>\n",
       "      <th>黑龙江省</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年份</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>25.30</td>\n",
       "      <td>18.09</td>\n",
       "      <td>23.80</td>\n",
       "      <td>7.53</td>\n",
       "      <td>22.61</td>\n",
       "      <td>42.87</td>\n",
       "      <td>21.69</td>\n",
       "      <td>4.69</td>\n",
       "      <td>32.36</td>\n",
       "      <td>42.15</td>\n",
       "      <td>...</td>\n",
       "      <td>45.65</td>\n",
       "      <td>9.30</td>\n",
       "      <td>14.70</td>\n",
       "      <td>1.63</td>\n",
       "      <td>13.66</td>\n",
       "      <td>39.55</td>\n",
       "      <td>12.07</td>\n",
       "      <td>21.06</td>\n",
       "      <td>4.23</td>\n",
       "      <td>41.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>25.63</td>\n",
       "      <td>19.19</td>\n",
       "      <td>24.77</td>\n",
       "      <td>7.43</td>\n",
       "      <td>23.18</td>\n",
       "      <td>54.36</td>\n",
       "      <td>22.52</td>\n",
       "      <td>5.00</td>\n",
       "      <td>31.45</td>\n",
       "      <td>43.07</td>\n",
       "      <td>...</td>\n",
       "      <td>47.29</td>\n",
       "      <td>9.71</td>\n",
       "      <td>14.35</td>\n",
       "      <td>1.69</td>\n",
       "      <td>14.09</td>\n",
       "      <td>40.96</td>\n",
       "      <td>13.42</td>\n",
       "      <td>22.35</td>\n",
       "      <td>4.22</td>\n",
       "      <td>39.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>24.81</td>\n",
       "      <td>19.47</td>\n",
       "      <td>25.87</td>\n",
       "      <td>7.85</td>\n",
       "      <td>23.88</td>\n",
       "      <td>54.64</td>\n",
       "      <td>25.08</td>\n",
       "      <td>4.94</td>\n",
       "      <td>30.91</td>\n",
       "      <td>43.69</td>\n",
       "      <td>...</td>\n",
       "      <td>45.10</td>\n",
       "      <td>9.48</td>\n",
       "      <td>15.41</td>\n",
       "      <td>1.77</td>\n",
       "      <td>14.49</td>\n",
       "      <td>46.15</td>\n",
       "      <td>14.26</td>\n",
       "      <td>22.35</td>\n",
       "      <td>4.44</td>\n",
       "      <td>40.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>24.26</td>\n",
       "      <td>20.10</td>\n",
       "      <td>26.71</td>\n",
       "      <td>7.99</td>\n",
       "      <td>25.72</td>\n",
       "      <td>56.26</td>\n",
       "      <td>25.77</td>\n",
       "      <td>5.10</td>\n",
       "      <td>30.45</td>\n",
       "      <td>45.84</td>\n",
       "      <td>...</td>\n",
       "      <td>44.94</td>\n",
       "      <td>9.77</td>\n",
       "      <td>16.26</td>\n",
       "      <td>1.84</td>\n",
       "      <td>14.78</td>\n",
       "      <td>47.33</td>\n",
       "      <td>15.68</td>\n",
       "      <td>22.74</td>\n",
       "      <td>4.58</td>\n",
       "      <td>39.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>22.06</td>\n",
       "      <td>19.81</td>\n",
       "      <td>27.08</td>\n",
       "      <td>8.10</td>\n",
       "      <td>26.27</td>\n",
       "      <td>55.78</td>\n",
       "      <td>26.00</td>\n",
       "      <td>5.07</td>\n",
       "      <td>28.99</td>\n",
       "      <td>45.75</td>\n",
       "      <td>...</td>\n",
       "      <td>44.46</td>\n",
       "      <td>9.65</td>\n",
       "      <td>17.15</td>\n",
       "      <td>1.95</td>\n",
       "      <td>14.90</td>\n",
       "      <td>42.72</td>\n",
       "      <td>14.26</td>\n",
       "      <td>23.44</td>\n",
       "      <td>4.67</td>\n",
       "      <td>39.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "地区      上海市    云南省  内蒙古自治区   北京市    吉林省    四川省    天津市  宁夏回族自治区    安徽省    山东省  \\\n",
       "年份                                                                             \n",
       "2013  25.30  18.09   23.80  7.53  22.61  42.87  21.69     4.69  32.36  42.15   \n",
       "2014  25.63  19.19   24.77  7.43  23.18  54.36  22.52     5.00  31.45  43.07   \n",
       "2015  24.81  19.47   25.87  7.85  23.88  54.64  25.08     4.94  30.91  43.69   \n",
       "2016  24.26  20.10   26.71  7.99  25.72  56.26  25.77     5.10  30.45  45.84   \n",
       "2017  22.06  19.81   27.08  8.10  26.27  55.78  26.00     5.07  28.99  45.75   \n",
       "\n",
       "地区    ...    湖南省   甘肃省    福建省  西藏自治区    贵州省    辽宁省    重庆市    陕西省   青海省   黑龙江省  \n",
       "年份    ...                                                                      \n",
       "2013  ...  45.65  9.30  14.70   1.63  13.66  39.55  12.07  21.06  4.23  41.37  \n",
       "2014  ...  47.29  9.71  14.35   1.69  14.09  40.96  13.42  22.35  4.22  39.85  \n",
       "2015  ...  45.10  9.48  15.41   1.77  14.49  46.15  14.26  22.35  4.44  40.98  \n",
       "2016  ...  44.94  9.77  16.26   1.84  14.78  47.33  15.68  22.74  4.58  39.58  \n",
       "2017  ...  44.46  9.65  17.15   1.95  14.90  42.72  14.26  23.44  4.67  39.74  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "失业人数 = df.groupby(by= [\"地区\",\"年份\"]) \\\n",
    "                  .agg({\"城镇登记失业人数(万人)\":\"sum\"}) \\\n",
    "                  .reset_index() \\\n",
    "                  .set_index([\"年份\"]) \\\n",
    "                  .pivot(columns=\"地区\", values=\"城镇登记失业人数(万人)\")\n",
    "\n",
    "失业人数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12099c208>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.sans-serif'] = ['Songti SC']\n",
    "失业人数.plot(figsize=(16,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 结论"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 各省份中事业人数变动最大的是贵州省，在2013年-2014年失业人数急速上涨，并且一直保持巨高不下的水平，可能发生巨大变动\n",
    "- 因此毕业生想选择贵族省定居的需要着重考虑下将来的失业问题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 幸福指数分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- numpy是Python的一种用于存储和处理大型矩阵的工具\n",
    "- 有关numpy的知识，[百度百科给予的参考资料](https://baike.baidu.com/item/numpy/5678437?fr=aladdin)\n",
    "- 有关numpy的使用[numpy的菜鸟教程](https://www.runoob.com/numpy/numpy-tutorial.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "//anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 32467 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "//anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 23130 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "//anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 30331 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "//anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 35760 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "//anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 19975 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "//anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 23545 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "//anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 32467 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "//anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 23130 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "//anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 30331 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "//anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 35760 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "//anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 19975 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "//anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 23545 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "//anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 31163 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "//anaconda3/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 31163 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 这两行代码解决 plt 中文显示的问题\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 输入产量与温度数据\n",
    "jh = [15.15, 9.51, 50.49, 18.7, 10.87, 62.58, 75.81]\n",
    "lh = [8.06, 5.89, 23.24, 10.09, 5.88, 27.25, 22.03]\n",
    "#city = [北京市, 天津市, 河北省, 内蒙古自治区, 上海市, 山东省, 广东省]\n",
    "lhv = [3.71, 3.78, 3.1, 4, 2.43, 2.58, 1.99]\n",
    "\n",
    "colors = np.random.rand(len(lh))  # 颜色数组\n",
    "#size = production\n",
    "plt.scatter(jh, lh, s=200, c=colors, alpha=0.6)  # 画散点图, alpha=0.6 表示不透明度为 0.6\n",
    "plt.ylim([0, 100])  # 纵坐标轴范围\n",
    "plt.xlim([0, 30])   # 横坐标轴范围\n",
    "plt.xlabel('结婚登记(万对)')  # 横坐标轴标题\n",
    "plt.ylabel('离婚登记(万对)')  # 纵坐标轴标题\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 结论"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 对所选的几个经济较发达或者生活环境较好的省份相比较幸福率\n",
    "- 其中结果为粗离婚率较高的为内蒙古自治区\n",
    "- 粗离婚率较低的为广东省\n",
    "- 追求生活幸福美满的毕业生可以考虑向广东省定居"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 洪涝等灾害受灾面积概览"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 有关pyecharts的使用教程：[Python可视化神器——pyecharts的超详细使用指南！](https://www.jianshu.com/p/554d64470ec9)，感谢分享！\n",
    "- 可供参考：[pyecharts的相关说明文档](http://pyecharts.herokuapp.com/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/apple/Desktop/作业/二下pandas/洪涝等灾害受灾面积概览.html'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts.faker import Faker\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Page, Pie\n",
    "l1 = [\"河北省\", \"陕西省\", \"内蒙古自治区\", \"辽宁省\",\"青海省\",\"甘肃省\",]\n",
    "num =[1678, 576.2, 1280.3, 620.1,57.9,701.3]\n",
    "c = (\n",
    "        Pie()\n",
    "        .add(\n",
    "            \"\",\n",
    "            [list(z) for z in zip(l1, num)],\n",
    "            radius=[\"40%\", \"65%\"],   # 圆环的粗细和大小\n",
    "        )\n",
    "        .set_global_opts(\n",
    "            title_opts=opts.TitleOpts(title=\"洪涝、山体滑坡、泥石流和台风受灾面积(千公顷)\",pos_right= 'center',pos_left= 'center'),\n",
    "            legend_opts=opts.LegendOpts(\n",
    "                orient=\"vertical\", pos_top=\"20%\", pos_left=\"20%\"  # 左面比例尺\n",
    "            ),\n",
    "        )\n",
    "        .set_series_opts(label_opts=opts.LabelOpts(formatter=\"{b}: {c}\"))\n",
    "    )\n",
    "c.render_notebook()\n",
    "c.render(\"洪涝等灾害受灾面积概览.html\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 地震多发地区受灾次数概览"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 有关[python 中条形图绘制](https://www.cnblogs.com/LiErRui/articles/11580851.html)，感谢分享！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/apple/Desktop/作业/二下pandas/地震多发地区受灾次数概览.html'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Bar\n",
    "from random import randint\n",
    "\n",
    "\n",
    "def bar_series() -> Bar:\n",
    "    c = (\n",
    "        Bar()\n",
    "        .add_xaxis([\"四川\", \"西藏自治区\", \"云南\", \"新疆维吾尔自治区\",\"青海省\"])\n",
    "        .add_yaxis(\"预警次数\", [11,7,16,22,5])\n",
    "        .set_global_opts(title_opts=opts.TitleOpts(title=\"地震多发地区受灾次数\",pos_left= '20%',pos_right='50%'))\n",
    "    )\n",
    "    return c\n",
    "bar_series().render_notebook()\n",
    "bar_series().render(\"地震多发地区受灾次数概览.html\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2017年年末常住人口"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 有关[利用Python绘制南丁格尔图](https://blog.csdn.net/qq_42169061/article/details/105267022)，感谢分享！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/apple/Desktop/作业/二下pandas/2017年年末常住人口.html'"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pyecharts.charts import Pie\n",
    "from pyecharts import options as opts\n",
    "\n",
    "color_series = ['#1fa2ff','#45b1ff','#7dc9ff','#33dbff','#6de5ff', '#329e71','#45b888','#3dd395','#3dd395','#3dd395','#3dd395']\n",
    "sh = [\"北京市\",\"天津市\",\"河北省\",\"辽宁省\",\"黑龙江省\",\"上海市\",\"江苏省\",\"山东省\",\"河南省\",\"广东省\"] \n",
    "num = [2171,1557,7520,4369,3789,2418,8029,10006,9559,11169]\n",
    "\n",
    "二级 = pd.DataFrame({'sh': sh, 'num': num})\n",
    "二级.sort_values(by='num', ascending=False, inplace=True)\n",
    "v = 二级['sh'].values.tolist()\n",
    "d = 二级['num'].values.tolist()\n",
    "\n",
    "pie1 = Pie(init_opts=opts.InitOpts(width='1000px', height='600px'))\n",
    "\n",
    "pie1.set_colors(color_series)\n",
    "# 添加数据，设置饼图的半径，是否展示成南丁格尔图\n",
    "pie1.add(\"\", [list(z) for z in zip(v, d)],\n",
    "        radius=[\"36%\", \"100%\"],\n",
    "        center=[\"50%\", \"60%\"],\n",
    "        rosetype=\"area\"\n",
    "        )\n",
    "pie1.set_global_opts(title_opts=opts.TitleOpts(title='2017年', subtitle='年末常住人口',\n",
    "                                               title_textstyle_opts=opts.TextStyleOpts(font_size=25,color= '#0085c3'),\n",
    "                                               subtitle_textstyle_opts= opts.TextStyleOpts(font_size=30,color= '#003399'),\n",
    "                                               pos_right= 'center',pos_left= 'center',pos_top= '53%',pos_bottom='center'\n",
    "                                              ),\n",
    "                     legend_opts=opts.LegendOpts(is_show=False),\n",
    "                     toolbox_opts=opts.ToolboxOpts())\n",
    "\n",
    "\n",
    "pie1.set_series_opts(label_opts=opts.LabelOpts(is_show=True, position=\"inside\", font_size=18,\n",
    "                                               formatter=\"{b}:{c}万人\", font_style=\"italic\",\n",
    "                                               font_weight=\"bold\", font_family=\"Microsoft YaHei\"\n",
    "                                               ),\n",
    "                     )\n",
    "pie1.render('2017年年末常住人口.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 城镇单位就业人员平均工资"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 有关[漏斗图制作参考链接](https://www.jb51.net/article/157296.htm)，感谢分享！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/apple/Desktop/作业/二下pandas/城镇单位就业人员平均工资漏斗图.html'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Funnel, Page\n",
    "from random import randint\n",
    "\n",
    "shs =['北京市', '天津市', '上海市', '河南省', '重庆市', '广东省', '海南省', '河北省', '山西省', '内蒙古自治区']\n",
    "values =[131700,94534,129795,55495,70889,79183,67727,63036,41501,66679]\n",
    "\n",
    "def funnel_base() -> Funnel:\n",
    "    c = (\n",
    "        Funnel()\n",
    "        .add(\"元\", [list(z) for z in zip(attr,values)])\n",
    "        .set_global_opts(\n",
    "            title_opts=opts.TitleOpts(title=\"城镇单位就业人员平均工资(元)\",pos_right= 'center',pos_left= 'center',pos_top='5%'),\n",
    "        legend_opts=opts.LegendOpts(\n",
    "                orient=\"vertical\", pos_top=\"30%\", pos_left=\"3%\"  # 左面比例尺\n",
    "            ),)\n",
    "        \n",
    "    )\n",
    "    return c\n",
    "    \n",
    "funnel_base().render_notebook()\n",
    "funnel_base().render('城镇单位就业人员平均工资漏斗图.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 出生率与死亡率概述"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 有关[饼状图制作参考链接](http://gallery.pyecharts.org/#/Pie/pie_radius)，感谢分享！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/apple/Desktop/作业/二下pandas/2017年不同省出生率概览.html'"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shss = [\"北京市\", \"天津市\", \"上海市\", \"河南省\", \"重庆市\",\"广东省\",\"海南省\", \"河北省\", \"山西省\",\"内蒙古自治区\"]\n",
    "radar_birth = [[9.06, 7.65, 11.06, 8.1, 12.95, 11.18, 13.68, 14.73, 13.2, 9.47]]\n",
    "c = (\n",
    "        Pie()\n",
    "        .add(\n",
    "            \"\",\n",
    "            [list(z) for z in zip(l1, num)],\n",
    "            radius=[\"0\", \"60%\"],   # 圆环的粗细和大小\n",
    "        )\n",
    "        .set_global_opts(\n",
    "            title_opts=opts.TitleOpts(title=\"2017年不同省出生率\",pos_right= 'center',pos_left= 'center'),\n",
    "            legend_opts=opts.LegendOpts(\n",
    "                orient=\"vertical\", pos_top=\"20%\", pos_left=\"20%\"  # 左面比例尺\n",
    "            ),\n",
    "        )\n",
    "        .set_series_opts(label_opts=opts.LabelOpts(formatter=\"{b}: {c}\"))\n",
    "    )\n",
    "c.render_notebook()\n",
    "c.render(\"2017年不同省出生率概览.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/apple/Desktop/作业/二下pandas/2017年不同省死亡率概览.html'"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shss = [\"北京市\", \"天津市\", \"上海市\", \"河南省\", \"重庆市\",\"广东省\",\"海南省\", \"河北省\", \"山西省\",\"内蒙古自治区\"]\n",
    "radar_death = [[5.3, 5.05, 5.45, 5.3, 6.97, 7.27, 4.52, 6.01, 6.6, 5.74]]\n",
    "c = (\n",
    "        Pie()\n",
    "        .add(\n",
    "            \"\",\n",
    "            [list(z) for z in zip(l1, num)],\n",
    "            radius=[\"0\", \"60%\"],   # 圆环的粗细和大小\n",
    "        )\n",
    "        .set_global_opts(\n",
    "            title_opts=opts.TitleOpts(title=\"2017年不同省死亡率\",pos_right= 'center',pos_left= 'center'),\n",
    "            legend_opts=opts.LegendOpts(\n",
    "                orient=\"vertical\", pos_top=\"20%\", pos_left=\"20%\"  # 左面比例尺\n",
    "            ),\n",
    "        )\n",
    "        .set_series_opts(label_opts=opts.LabelOpts(formatter=\"{b}: {c}\"))\n",
    "   )\n",
    "c.render_notebook()\n",
    "c.render(\"2017年不同省死亡率概览.html\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绿化覆盖分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 有关时间的函数的用法[Python之datetime模块](https://www.cnblogs.com/zhaopanpan/p/9021951.html)\n",
    "- 有关地图的可视化[地图使用参考](http://gallery.pyecharts.org/#/Map/map_china_cities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>建成区绿化覆盖率(%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>地区</th>\n",
       "      <th>年份</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">上海市</th>\n",
       "      <th>2013</th>\n",
       "      <td>38.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>38.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>38.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>38.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>39.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">黑龙江省</th>\n",
       "      <th>2013</th>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>35.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>35.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>35.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>155 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           建成区绿化覆盖率(%)\n",
       "地区   年份               \n",
       "上海市  2013         38.4\n",
       "     2014         38.4\n",
       "     2015         38.5\n",
       "     2016         38.6\n",
       "     2017         39.1\n",
       "...                ...\n",
       "黑龙江省 2013         36.0\n",
       "     2014         36.0\n",
       "     2015         35.8\n",
       "     2016         35.4\n",
       "     2017         35.5\n",
       "\n",
       "[155 rows x 1 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "绿化覆盖 = df.groupby(by= [\"地区\",\"年份\"])\\\n",
    "                         .agg({\"建成区绿化覆盖率(%)\":\"sum\"})\\\n",
    "\n",
    "#                         .rename(columns={\"独角兽\":\"数量\"})\n",
    "绿化覆盖#.数量.to_dict() #.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">建成区绿化覆盖率(%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>地区</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>青海省</th>\n",
       "      <td>29.8</td>\n",
       "      <td>32.6</td>\n",
       "      <td>31.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>甘肃省</th>\n",
       "      <td>30.2</td>\n",
       "      <td>33.3</td>\n",
       "      <td>31.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西藏自治区</th>\n",
       "      <td>18.1</td>\n",
       "      <td>43.8</td>\n",
       "      <td>34.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>吉林省</th>\n",
       "      <td>31.4</td>\n",
       "      <td>36.1</td>\n",
       "      <td>34.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贵州省</th>\n",
       "      <td>34.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>35.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>黑龙江省</th>\n",
       "      <td>35.4</td>\n",
       "      <td>36.0</td>\n",
       "      <td>35.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>天津市</th>\n",
       "      <td>34.9</td>\n",
       "      <td>37.2</td>\n",
       "      <td>36.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新疆维吾尔自治区</th>\n",
       "      <td>36.4</td>\n",
       "      <td>40.0</td>\n",
       "      <td>37.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>湖北省</th>\n",
       "      <td>37.5</td>\n",
       "      <td>38.4</td>\n",
       "      <td>37.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云南省</th>\n",
       "      <td>37.3</td>\n",
       "      <td>38.9</td>\n",
       "      <td>37.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广西壮族自治区</th>\n",
       "      <td>37.6</td>\n",
       "      <td>39.3</td>\n",
       "      <td>38.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>河南省</th>\n",
       "      <td>37.6</td>\n",
       "      <td>39.4</td>\n",
       "      <td>38.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>上海市</th>\n",
       "      <td>38.4</td>\n",
       "      <td>39.1</td>\n",
       "      <td>38.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>四川省</th>\n",
       "      <td>37.5</td>\n",
       "      <td>40.0</td>\n",
       "      <td>38.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>宁夏回族自治区</th>\n",
       "      <td>37.9</td>\n",
       "      <td>40.4</td>\n",
       "      <td>39.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>内蒙古自治区</th>\n",
       "      <td>36.2</td>\n",
       "      <td>40.2</td>\n",
       "      <td>39.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>湖南省</th>\n",
       "      <td>37.6</td>\n",
       "      <td>41.2</td>\n",
       "      <td>39.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>辽宁省</th>\n",
       "      <td>36.4</td>\n",
       "      <td>40.7</td>\n",
       "      <td>39.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>山西省</th>\n",
       "      <td>40.0</td>\n",
       "      <td>40.6</td>\n",
       "      <td>40.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>陕西省</th>\n",
       "      <td>39.9</td>\n",
       "      <td>40.6</td>\n",
       "      <td>40.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>海南省</th>\n",
       "      <td>37.7</td>\n",
       "      <td>42.1</td>\n",
       "      <td>40.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>浙江省</th>\n",
       "      <td>40.3</td>\n",
       "      <td>41.0</td>\n",
       "      <td>40.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>重庆市</th>\n",
       "      <td>40.3</td>\n",
       "      <td>41.7</td>\n",
       "      <td>40.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>安徽省</th>\n",
       "      <td>39.9</td>\n",
       "      <td>42.2</td>\n",
       "      <td>41.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>河北省</th>\n",
       "      <td>40.8</td>\n",
       "      <td>41.9</td>\n",
       "      <td>41.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广东省</th>\n",
       "      <td>41.4</td>\n",
       "      <td>43.5</td>\n",
       "      <td>42.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>山东省</th>\n",
       "      <td>42.1</td>\n",
       "      <td>42.8</td>\n",
       "      <td>42.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>江苏省</th>\n",
       "      <td>42.4</td>\n",
       "      <td>43.0</td>\n",
       "      <td>42.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>福建省</th>\n",
       "      <td>42.8</td>\n",
       "      <td>43.7</td>\n",
       "      <td>43.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>江西省</th>\n",
       "      <td>43.6</td>\n",
       "      <td>45.2</td>\n",
       "      <td>44.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京市</th>\n",
       "      <td>47.1</td>\n",
       "      <td>49.1</td>\n",
       "      <td>48.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         建成区绿化覆盖率(%)             \n",
       "                 min   max   mean\n",
       "地区                               \n",
       "青海省             29.8  32.6  31.26\n",
       "甘肃省             30.2  33.3  31.58\n",
       "西藏自治区           18.1  43.8  34.38\n",
       "吉林省             31.4  36.1  34.82\n",
       "贵州省             34.0  37.0  35.64\n",
       "黑龙江省            35.4  36.0  35.74\n",
       "天津市             34.9  37.2  36.04\n",
       "新疆维吾尔自治区        36.4  40.0  37.84\n",
       "湖北省             37.5  38.4  37.90\n",
       "云南省             37.3  38.9  37.98\n",
       "广西壮族自治区         37.6  39.3  38.26\n",
       "河南省             37.6  39.4  38.46\n",
       "上海市             38.4  39.1  38.60\n",
       "四川省             37.5  40.0  38.90\n",
       "宁夏回族自治区         37.9  40.4  39.04\n",
       "内蒙古自治区          36.2  40.2  39.06\n",
       "湖南省             37.6  41.2  39.54\n",
       "辽宁省             36.4  40.7  39.54\n",
       "山西省             40.0  40.6  40.26\n",
       "陕西省             39.9  40.6  40.26\n",
       "海南省             37.7  42.1  40.30\n",
       "浙江省             40.3  41.0  40.62\n",
       "重庆市             40.3  41.7  40.74\n",
       "安徽省             39.9  42.2  41.24\n",
       "河北省             40.8  41.9  41.38\n",
       "广东省             41.4  43.5  42.04\n",
       "山东省             42.1  42.8  42.42\n",
       "江苏省             42.4  43.0  42.74\n",
       "福建省             42.8  43.7  43.12\n",
       "江西省             43.6  45.2  44.52\n",
       "北京市             47.1  49.1  48.28"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 各地区人员平均工资排序\n",
    "绿化 = 绿化覆盖.groupby([\"地区\"]).agg({\"建成区绿化覆盖率(%)\":[\"min\",\"max\",\"mean\"]}).sort_values(by=(\"建成区绿化覆盖率(%)\",\"mean\"))\n",
    "绿化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('建成区绿化覆盖率(%)',\n",
       "  {('上海市', 2013): 38.4,\n",
       "   ('上海市', 2014): 38.4,\n",
       "   ('上海市', 2015): 38.5,\n",
       "   ('上海市', 2016): 38.6,\n",
       "   ('上海市', 2017): 39.1,\n",
       "   ('云南省', 2013): 37.8,\n",
       "   ('云南省', 2014): 38.1,\n",
       "   ('云南省', 2015): 37.3,\n",
       "   ('云南省', 2016): 37.8,\n",
       "   ('云南省', 2017): 38.9,\n",
       "   ('内蒙古自治区', 2013): 36.2,\n",
       "   ('内蒙古自治区', 2014): 39.8,\n",
       "   ('内蒙古自治区', 2015): 39.2,\n",
       "   ('内蒙古自治区', 2016): 39.9,\n",
       "   ('内蒙古自治区', 2017): 40.2,\n",
       "   ('北京市', 2013): 47.1,\n",
       "   ('北京市', 2014): 49.1,\n",
       "   ('北京市', 2015): 48.4,\n",
       "   ('北京市', 2016): 48.4,\n",
       "   ('北京市', 2017): 48.4,\n",
       "   ('吉林省', 2013): 31.4,\n",
       "   ('吉林省', 2014): 35.8,\n",
       "   ('吉林省', 2015): 36.1,\n",
       "   ('吉林省', 2016): 35.0,\n",
       "   ('吉林省', 2017): 35.8,\n",
       "   ('四川省', 2013): 38.4,\n",
       "   ('四川省', 2014): 37.5,\n",
       "   ('四川省', 2015): 38.7,\n",
       "   ('四川省', 2016): 39.9,\n",
       "   ('四川省', 2017): 40.0,\n",
       "   ('天津市', 2013): 34.9,\n",
       "   ('天津市', 2014): 34.9,\n",
       "   ('天津市', 2015): 36.4,\n",
       "   ('天津市', 2016): 37.2,\n",
       "   ('天津市', 2017): 36.8,\n",
       "   ('宁夏回族自治区', 2013): 38.5,\n",
       "   ('宁夏回族自治区', 2014): 38.0,\n",
       "   ('宁夏回族自治区', 2015): 37.9,\n",
       "   ('宁夏回族自治区', 2016): 40.4,\n",
       "   ('宁夏回族自治区', 2017): 40.4,\n",
       "   ('安徽省', 2013): 39.9,\n",
       "   ('安徽省', 2014): 41.2,\n",
       "   ('安徽省', 2015): 41.2,\n",
       "   ('安徽省', 2016): 41.7,\n",
       "   ('安徽省', 2017): 42.2,\n",
       "   ('山东省', 2013): 42.6,\n",
       "   ('山东省', 2014): 42.8,\n",
       "   ('山东省', 2015): 42.3,\n",
       "   ('山东省', 2016): 42.3,\n",
       "   ('山东省', 2017): 42.1,\n",
       "   ('山西省', 2013): 40.0,\n",
       "   ('山西省', 2014): 40.1,\n",
       "   ('山西省', 2015): 40.1,\n",
       "   ('山西省', 2016): 40.5,\n",
       "   ('山西省', 2017): 40.6,\n",
       "   ('广东省', 2013): 41.5,\n",
       "   ('广东省', 2014): 41.4,\n",
       "   ('广东省', 2015): 41.4,\n",
       "   ('广东省', 2016): 42.4,\n",
       "   ('广东省', 2017): 43.5,\n",
       "   ('广西壮族自治区', 2013): 37.7,\n",
       "   ('广西壮族自治区', 2014): 39.3,\n",
       "   ('广西壮族自治区', 2015): 37.6,\n",
       "   ('广西壮族自治区', 2016): 37.6,\n",
       "   ('广西壮族自治区', 2017): 39.1,\n",
       "   ('新疆维吾尔自治区', 2013): 36.4,\n",
       "   ('新疆维吾尔自治区', 2014): 36.8,\n",
       "   ('新疆维吾尔自治区', 2015): 37.5,\n",
       "   ('新疆维吾尔自治区', 2016): 38.5,\n",
       "   ('新疆维吾尔自治区', 2017): 40.0,\n",
       "   ('江苏省', 2013): 42.4,\n",
       "   ('江苏省', 2014): 42.6,\n",
       "   ('江苏省', 2015): 42.8,\n",
       "   ('江苏省', 2016): 42.9,\n",
       "   ('江苏省', 2017): 43.0,\n",
       "   ('江西省', 2013): 45.1,\n",
       "   ('江西省', 2014): 44.6,\n",
       "   ('江西省', 2015): 44.1,\n",
       "   ('江西省', 2016): 43.6,\n",
       "   ('江西省', 2017): 45.2,\n",
       "   ('河北省', 2013): 41.2,\n",
       "   ('河北省', 2014): 41.9,\n",
       "   ('河北省', 2015): 41.2,\n",
       "   ('河北省', 2016): 40.8,\n",
       "   ('河北省', 2017): 41.8,\n",
       "   ('河南省', 2013): 37.6,\n",
       "   ('河南省', 2014): 38.3,\n",
       "   ('河南省', 2015): 37.7,\n",
       "   ('河南省', 2016): 39.3,\n",
       "   ('河南省', 2017): 39.4,\n",
       "   ('浙江省', 2013): 40.3,\n",
       "   ('浙江省', 2014): 40.8,\n",
       "   ('浙江省', 2015): 40.6,\n",
       "   ('浙江省', 2016): 41.0,\n",
       "   ('浙江省', 2017): 40.4,\n",
       "   ('海南省', 2013): 42.1,\n",
       "   ('海南省', 2014): 41.3,\n",
       "   ('海南省', 2015): 37.7,\n",
       "   ('海南省', 2016): 40.3,\n",
       "   ('海南省', 2017): 40.1,\n",
       "   ('湖北省', 2013): 38.1,\n",
       "   ('湖北省', 2014): 37.9,\n",
       "   ('湖北省', 2015): 37.5,\n",
       "   ('湖北省', 2016): 37.6,\n",
       "   ('湖北省', 2017): 38.4,\n",
       "   ('湖南省', 2013): 37.6,\n",
       "   ('湖南省', 2014): 38.6,\n",
       "   ('湖南省', 2015): 39.7,\n",
       "   ('湖南省', 2016): 40.6,\n",
       "   ('湖南省', 2017): 41.2,\n",
       "   ('甘肃省', 2013): 32.1,\n",
       "   ('甘肃省', 2014): 30.8,\n",
       "   ('甘肃省', 2015): 30.2,\n",
       "   ('甘肃省', 2016): 31.5,\n",
       "   ('甘肃省', 2017): 33.3,\n",
       "   ('福建省', 2013): 42.8,\n",
       "   ('福建省', 2014): 42.8,\n",
       "   ('福建省', 2015): 43.0,\n",
       "   ('福建省', 2016): 43.3,\n",
       "   ('福建省', 2017): 43.7,\n",
       "   ('西藏自治区', 2013): 18.1,\n",
       "   ('西藏自治区', 2014): 43.8,\n",
       "   ('西藏自治区', 2015): 42.6,\n",
       "   ('西藏自治区', 2016): 32.6,\n",
       "   ('西藏自治区', 2017): 34.8,\n",
       "   ('贵州省', 2013): 34.5,\n",
       "   ('贵州省', 2014): 34.0,\n",
       "   ('贵州省', 2015): 35.9,\n",
       "   ('贵州省', 2016): 36.8,\n",
       "   ('贵州省', 2017): 37.0,\n",
       "   ('辽宁省', 2013): 40.2,\n",
       "   ('辽宁省', 2014): 40.1,\n",
       "   ('辽宁省', 2015): 40.3,\n",
       "   ('辽宁省', 2016): 36.4,\n",
       "   ('辽宁省', 2017): 40.7,\n",
       "   ('重庆市', 2013): 41.7,\n",
       "   ('重庆市', 2014): 40.6,\n",
       "   ('重庆市', 2015): 40.3,\n",
       "   ('重庆市', 2016): 40.8,\n",
       "   ('重庆市', 2017): 40.3,\n",
       "   ('陕西省', 2013): 40.2,\n",
       "   ('陕西省', 2014): 40.5,\n",
       "   ('陕西省', 2015): 40.6,\n",
       "   ('陕西省', 2016): 40.1,\n",
       "   ('陕西省', 2017): 39.9,\n",
       "   ('青海省', 2013): 31.2,\n",
       "   ('青海省', 2014): 31.6,\n",
       "   ('青海省', 2015): 29.8,\n",
       "   ('青海省', 2016): 31.1,\n",
       "   ('青海省', 2017): 32.6,\n",
       "   ('黑龙江省', 2013): 36.0,\n",
       "   ('黑龙江省', 2014): 36.0,\n",
       "   ('黑龙江省', 2015): 35.8,\n",
       "   ('黑龙江省', 2016): 35.4,\n",
       "   ('黑龙江省', 2017): 35.5})]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_world = [(k, v) for k, v in 绿化覆盖.to_dict().items()]  \n",
    "data_world"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 有关[有关基础地图的绘制运用](http://gallery.pyecharts.org/#/Map/map_base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<pyecharts.render.display.HTML at 0x120999898>"
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     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pyecharts Geo：地理坐标系\n",
    "from pyecharts.charts import Geo\n",
    "from pyecharts.faker import Faker\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Geo\n",
    "from pyecharts.globals import ChartType, SymbolType\n",
    "def geo_base() -> Geo:\n",
    "    c = (\n",
    "        Geo()\n",
    "        .add_schema(maptype=\"china\")\n",
    "        .add(\"geo\", [list(z) for z in zip(Faker.provinces, Faker.values())])\n",
    "        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n",
    "        .set_global_opts(\n",
    "            visualmap_opts=opts.VisualMapOpts(),\n",
    "            title_opts=opts.TitleOpts(title=\"中国地区绿化覆盖\"),\n",
    "        )\n",
    "    )\n",
    "    return c\n",
    "\n",
    "中国地区绿化覆盖 = geo_base()\n",
    "中国地区绿化覆盖.render_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 结论"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 中国各省份的绿化建设普遍较好\n",
    "- 各省份的绿化覆盖率也在逐年稳步上升"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 总结"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 省份选择\n",
    "- 对于即将毕业或刚毕业的大学生而言，在那个省份/城市定居生活工作是需要仔细考虑的，工作地的选择直接影响到今后的生活与发展。针对这一痛点，我将从国家数据库得来的数据进行筛选处理与分析，得到了如上的报告，仅供参考。\n",
    "- 广东、山东、河南作为人口大省，对人们的包容性更大，但同时就业压力更大\n",
    "- 沿海地区的生产总值较高，北上广依旧是发展个人能力与实现经济自由的不二选择\n",
    "- 黑龙江、吉林、重庆的离婚率虽然在全国而言较高，但也不能代表这里的人们不幸福\n",
    "- 毕业后工作地的选择要根据自己和家人的意愿以及当地的各方面发展作为条件，希望每位毕业生都有光明美好的前途\n",
    "\n",
    "## 心得\n",
    "我想对在同行/同侪说：本项目具体实践中，我确实有很多的心得与体会，作为一个项目的分析者，要时时刻刻站在“以人为本”，只有站在人文的角度思考，对于数据的分析与理解才会有意义，才能帮助用户解决痛点，这些数字、文本、符号才有它们独特的意义。就此次的数据分析而言，数据分析的流程只是一些代码、方法的运用，而数据分析背后的含义才是数据分析真正的价值所在：它帮助我们更清晰直观地看到数据的含义，看到一个个数据所带来的信息，让我们在数字化高度发展的时代，以一种更便捷快速且清晰有趣的方式，得到对人、对社会、对世界的思考。\n",
    "\n",
    "## 感谢\n",
    "我的代码可供同行/同侪们参考\n",
    "感谢辛苦的廖老师和许老师和其他老师们，感谢帅哥美女们的耐心教导\n",
    "感谢同学们对我的项目的指导与建议，一起加油⛽️\n",
    "感谢网络的力量让我在迷茫的时候给我提供帮助，感谢愿意分享自己经验的大佬们 最后，设计思维及数据思维的融合的”混合心智模型”让我对于数据的分析有了更深刻的想法与理解，对于数据的处理与分析需要站在用户的角度，更贴近用户所需，在普遍用户群拥有的痛点基础上，进行对筛选出的数据的分析处理，设计出方便用户理解的图标，最终产生了符合用户需求的分析文档。 "
   ]
  }
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